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Abstract Three-dimensional (3D) marker-based motion capture is the current gold standard to assess and monitor pathological gait in a clinical setting. However, 3D markerless motion capture based on pose estimation is advancing into the field of gait analysis. This study aims at evaluating the lower-body 3D gait kinematics and kinetics from synchronously recorded Theia3D markerless and CAST marker-based systems. Twelve healthy individuals and 34 clinical patients aged 8–61 years walked at self-selected speed over a 13 m long walkway. Similarity between models was statistically analysed using inter-trial variability, root mean square error, Pearson’s correlation coefficient and Statistical Parametric Mapping. Inter-trial variability was on average higher for clinical patients in both models. Overall, the markerless system demonstrated similar gait patterns although hip and knee rotations were non-comparable. Pelvic anterior tilt was significantly underestimated. Significant differences especially in peak values at specific phases of the gait cycle were observed across all planes for all joints (more so for clinical patients than healthy subjects) as well as in the sagittal powers of the hip, knee and ankle. Theia3D markerless system offers great potential in gait analysis. This study brings awareness to potential clinical users and researchers where they can have confidence, as well as areas where caution should be exercised.

Background/Objectives: Comparing a given variable between the lower extremities (LEs) usually involves calculating the value of a selected asymmetry index. The aim of this study was to evaluate the mean-dependent asymmetry index for gait variables. Methods: The three-point crutch gait asymmetry between the non-surgical LE (NS) and surgical LE (S) was assessed in 14 patients after unilateral total hip arthroplasty. An eight-camera motion capture system integrated with two force platforms was used. The values of the new gait asymmetry index (MA) were calculated for such variables as stance phase time (ST), knee flexion and extension range of motion (KFE RoM), hip flexion and extension range of motion (HFE RoM), and vertical ground reaction force (VGRF). Results: An analysis related to gait asymmetry showed significantly higher values for all variables for the NS than for the S (the MA ranged from 9.9 to 42.0%; p < 0.001). In the case of comparisons between the MA and other indices, the intraclass correlation coefficient ranged from 0.566 to 0.998 (p < 0.001) with Bland–Altman bias values that ranged from −18.2 to 0.3 %GC (ST), from 0.0 to 0.5° (KFE RoM), from −12.4 to 1.4° (HFE RoM), and from −11.9 to −0.1 %BW (VGRF). Conclusions: The findings revealed a prominent three-point crutch gait asymmetry for all variables, especially a disturbingly large asymmetry for the HFE RoM and VGRF. The comparisons also showed generally excellent or good agreement with the other indices. Furthermore, the mean MA result from n single values was the same as the MA result calculated using the mean values of a given variable. The MA, as an accurate asymmetry index, can be used to objectively assess pathological gait asymmetry. [ABSTRACT FROM AUTHOR]

Aim: To investigate the dynamics of the motor control system during walking by examining the complexity, stability, and causal relationships of leg motions. Specifically, the study focuses on gait under both bilateral and unilateral constraints induced by a passive exoskeleton designed to replicate gastrocnemius contractures. Methods: Kinematic data was collected as 10 healthy participants walked at a self-selected speed. A new Complexity-Instability Index (CII) of the leg motions was defined as a function of the Correlation Dimension and the Largest Lyapunov Exponent. Causal interactions between the leg motions are explored using Convergent Cross Mapping. Results: Normal walking is characterized by a high mutual drive of each leg to the other, where CII is lowest for both legs (complexity of each leg motion is low and stability high). The effect of the bilateral emulated contractures is a reduced drive of each leg to the other and an increased CII for both legs. With unilateral emulated contracture, the mechanically constrained leg strongly drives the unconstrained leg, and CII was significantly higher for the constrained leg compared to normal walking. Conclusion: Redundancy in limb motions is used to support causal interactions, reducing complexity and increasing stability in our leg dynamics during walking. The role of redundancy is to allow adaptability above being able to satisfy the overall biomechanical problem; and to allow the system to interact optimally. From an applied perspective, important characteristics of functional movement patterns might be captured by these nonlinear and causal variables, as well as the biomechanical aspects typically studied. [ABSTRACT FROM AUTHOR]

AimTo investigate the dynamics of the motor control system during walking by examining the complexity, stability, and causal relationships of leg motions. Specifically, the study focuses on gait under both bilateral and unilateral constraints induced by a passive exoskeleton designed to replicate gastrocnemius contractures.MethodsKinematic data was collected as 10 healthy participants walked at a self-selected speed. A new Complexity-Instability Index (CII) of the leg motions was defined as a function of the Correlation Dimension and the Largest Lyapunov Exponent. Causal interactions between the leg motions are explored using Convergent Cross Mapping.ResultsNormal walking is characterized by a high mutual drive of each leg to the other, where CII is lowest for both legs (complexity of each leg motion is low and stability high). The effect of the bilateral emulated contractures is a reduced drive of each leg to the other and an increased CII for both legs. With unilateral emulated contracture, the mechanically constrained leg strongly drives the unconstrained leg, and CII was significantly higher for the constrained leg compared to normal walking.ConclusionRedundancy in limb motions is used to support causal interactions, reducing complexity and increasing stability in our leg dynamics during walking. The role of redundancy is to allow adaptability above being able to satisfy the overall biomechanical problem; and to allow the system to interact optimally. From an applied perspective, important characteristics of functional movement patterns might be captured by these nonlinear and causal variables, as well as the biomechanical aspects typically studied.

Injury or disease often compromise walking dynamics and negatively impact quality of life and independence. Assessing methods to restore or improve pathological gait can be expedited by examining a global parameter that reflects overall musculoskeletal control. Center of mass (CoM) kinematics follow well-defined trajectories during unimpaired gait, and change predictably with various gait pathologies. We propose a method to estimate CoM trajectories from inertial measurement units (IMUs) using a bidirectional Long Short-Term Memory neural network to evaluate rehabilitation interventions and outcomes. Five non-disabled volunteers participated in a single session of various dynamic walking trials with IMUs mounted on various body segments. A neural network trained with data from four of the five volunteers through a leave-one-subject out cross validation estimated the CoM with average root mean square errors (RMSEs) of 1.44cm, 1.15cm, and 0.40cm in the mediolateral (ML), anteroposterior (AP), and inferior/superior (IS) directions respectively. The impact of number and location of IMUs on network prediction accuracy was determined via principal component analysis. Comparing across all configurations, three to five IMUs located on the legs and medial trunk were the most promising reduced sensor sets for achieving CoM estimates suitable for outcome assessment. Lastly, the networks were tested on data from an individual with hemiparesis with the greatest error increase in the ML direction, which could stem from asymmetric gait. These results provide a framework for assessing gait deviations after disease or injury and evaluating rehabilitation interventions intended to normalize gait pathologies.

Instrumented gait analysis is an established procedure in biomechanical assessment, requiring specially-trained analysts to interpret the complex graphical output generated. Does a new method of visual representation of lower limb kinematic gait analysis data provide a reliable and valid method of interpretation of biomechanical data for healthcare professionals? An innovative system based on the Traffic Lights System (TLS) was developed. Simulated abnormal gait was captured using a 16-camera optoelectronic motion capture system, and the results were presented in both the Traditional Graphical System (TGS) format and the new TLS. An online form was filled by health professionals who attempted to interpret normal and abnormal motion in the joints presented in the 2 output formats. Out of 26 raters, 18 preferred the new system because of its user-friendliness and its ease of interpretation. 2 raters preferred the TGS, with one of these raters clarifying that the preference is due to colour blindness. For intra-rater reliability, 2 trained raters provided a second response for the TGS (Cronbach's Alpha ranging between 0.733 and 0.918), whilst the TLS resulted in Cronbach's Alpha between 0.817 and 1.00 amongst 3 untrained raters. The Fleiss Multi-rater Kappa Test demonstrated low inter-rater reliability amongst raters in the TGS, whereas the overall Fleiss Multi-rater Kappa values of the TLS surpassed the TGS in all 3 studies. This study showed that whilst trained health professionals have high intra-rater reliability in interpreting traditional gait analysis results, those professionals inexperienced in the system, do not always comprehend the complex graphs generated by the system when presenting gait analysis data. When these graphs are transformed into coloured outputs representing the extent of the movement, the TLS has demonstrated high validity and high intra- and inter-rater reliability, significantly exceeding those of the TGS, especially in untrained health professionals. • trained health professionals reliably interpret traditional gait analysis results. • inexperienced professionals do not comprehend complex graphs presenting gait analysis data. • TLS has demonstrated high validity and high intra- and inter-rater reliability. • reliability of TLS exceeded those of the TGS in untrained health professionals. [ABSTRACT FROM AUTHOR]

Injury or disease often compromise walking dynamics and negatively impact quality of life and independence. Assessing methods to restore or improve pathological gait can be expedited by examining a global parameter that reflects overall musculoskeletal control. Center of mass (CoM) kinematics follow well-defined trajectories during unimpaired gait, and change predictably with various gait pathologies. We propose a method to estimate CoM trajectories from inertial measurement units (IMUs) using a bidirectional Long Short-Term Memory neural network to evaluate rehabilitation interventions and outcomes. Five non-disabled volunteers participated in a single session of various dynamic walking trials with IMUs mounted on various body segments. A neural network trained with data from four of the five volunteers through a leave-one-subject out cross validation estimated the CoM with average root mean square errors (RMSEs) of 1.44cm, 1.15cm, and 0.40cm in the mediolateral (ML), anteroposterior (AP), and inferior/superior (IS) directions respectively. The impact of number and location of IMUs on network prediction accuracy was determined via principal component analysis. Comparing across all configurations, three to five IMUs located on the legs and medial trunk were the most promising reduced sensor sets for achieving CoM estimates suitable for outcome assessment. Lastly, the networks were tested on data from an individual with hemiparesis with the greatest error increase in the ML direction, which could stem from asymmetric gait. These results provide a framework for assessing gait deviations after disease or injury and evaluating rehabilitation interventions intended to normalize gait pathologies. [ABSTRACT FROM AUTHOR]

Introduction: The analysis of human gait is the subject of a numerous research studies with the aim of application in various fields of science - most often in sports and in medical practice. Of particular interest is research and analysis the information in the rehabilitation of a neurological conditions; post-traumatic conditions; systemic diseases; degenerative diseases, etc., which affect a large percentage of the population. The purpose of this scientific communication is to determine the need to provide an innovative technical device for registering gait parameters in patients with impaired locomotion. Material and methods: A survey was conducted among 41 specialists in the field of medical rehabilitation and occupational therapy, including: research on the needs of medico-social and rehabilitation activities to improve the social functioning of the person; determination of the need to provide a technical device recording gait parameters in patients with impaired locomotion. Results and analysis: As a result of the analysis the outcome from the conducted survey determines the need to carry out a precise measurement of gait parameters in patients with limited or difficult locomotion. This requires providing an appropriate innovative technical device for registering the spatio-temporal parameters of the gait. Modern scientific and practical developments reflect the latest achievements of science, technique, technology and practice, which requires their inclusion in clinical research, analysis of the data obtained from them and their application in daily clinical practice. Conclusion: In conclusion, it can be summarized that the use of innovative modern technical means to register gait parameters in patients with impaired locomotor activity leads to the precise measurement of multiple spatiotemporal characteristics and assessment of the patient's functional capacity, depending on age, gender and impairment. This requires a search for an opportunity to procure the necessary financial means, in order to provide an innovative technical device with an appropriate software program, through funding under a Scientific Research Project. [ABSTRACT FROM AUTHOR]

Three-Dimensional Gait Analysis (3DGA) is a gold standard tool that can help identify pathological components of walking patterns. It has been well established that this tool influences the treatment decision making of clinicians treating paediatric patients with Cerebral Palsy, but it has not been established whether this tool changes decision making of clinicians treating adults with complex pathological gait. To investigate the impact of pre-treatment 3DGA on treatment plans and management of adults with complex pathological gait. This retrospective audit examined the medical records of 87 patients undergoing pre-treatment 3DGA between 2014 and 2019. The review collected treatment plans from the initial referral, the post-gait analysis multidisciplinary report, and post-intervention progress notes with consistencies and differences noted throughout the care pathway. Treatment plans of patients were altered in 80 % (N = 32) of patients following 3DGA assessment and recommendations. These treatment plan alterations included a change in surgery or avoidance of surgery, changes in orthosis prescriptions, casting or rehabilitation; and administration or changes in administration of Botulinum Neurotoxin (BoNT-A). In 47 % (N = 15) of cases the change in plans represented a de-escalation in intervention requirements (e.g. BoNT-A in lieu of surgical intervention), and in 31 % (N = 10) the change in plans represented an escalation in intervention requirements (e.g. requirement for surgery). These changes in treatment plans were either fully or partly enacted by the referring consultant in 86 % of cases. Pre-treatment 3DGA impacts the management of adult patients with complex pathological gait and facilitates patients potentially avoiding unnecessary interventions. Further investigation is needed to determine the cost effectiveness of 3DGA in this population and the impact of pre-treatment 3DGA on management outcomes. • 3DGA alters management recommendations in adults with gait pathology. • The use of 3DGA in the adult population may reduce the incidence of potentially unnecessary interventions. • The cost effectiveness of 3DGA in this population should be investigated. [ABSTRACT FROM AUTHOR]

An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.9m/s and conditions such as long stride, short stride, asymmetric walking, stop-start, and abrupt speed changes. Spatial analysis of our method indicates an average RMSE of 1.74±0.23% and 2.35±0.52% in gait phase estimation of test participants in the treadmill and overground walking, respectively. The temporal analysis reveals that D67 detects heel-strike events with an average MAE of 1.70±0.54% and 2.74±0.92% of step duration on test participants in the treadmill and overground walking, respectively. Both spatial and temporal performances are uniform across participants and gait conditions. Further analyses indicate the robustness of the D67 to smooth and abrupt speed changes, limping, variation of stride length, and sudden start or stop of walking. The performance of the D67 is also compared to the state-of-the-art techniques confirming the superior and comparable performance of the D67 to techniques without and with a ground contact sensor, respectively. The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.

Abstract Background Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.9m/s and conditions such as long stride, short stride, asymmetric walking, stop-start, and abrupt speed changes. Spatial analysis of our method indicates an average RMSE of 1.74±0.23% and 2.35±0.52% in gait phase estimation of test participants in the treadmill and overground walking, respectively. The temporal analysis reveals that D67 detects heel-strike events with an average MAE of 1.70±0.54% and 2.74±0.92% of step duration on test participants in the treadmill and overground walking, respectively. Both spatial and temporal performances are uniform across participants and gait conditions. Further analyses indicate the robustness of the D67 to smooth and abrupt speed changes, limping, variation of stride length, and sudden start or stop of walking. The performance of the D67 is also compared to the state-of-the-art techniques confirming the superior and comparable performance of the D67 to techniques without and with a ground contact sensor, respectively. The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton. [ABSTRACT FROM AUTHOR]

This study investigates the pathological toe and heel gaits seen in human locomotion using neuromusculoskeletal modelling and simulation. In particular, it aims to investigate potential cause–effect relationships between biomechanical or neural impairments and pathological gaits. Toe and heel gaits are commonly present in spinal cord injury, stroke and cerebral palsy. Toe walking is mainly attributed to spasticity and contracture at plantar flexor muscles, whereas heel walking can be attributed to muscle weakness of biomechanical or neural origin. To investigate the effect of these impairments on gait, this study focuses on the soleus and gastrocnemius muscles as they contribute to ankle plantarflexion. We built a reflex circuit model based on previous work by Geyer and Herr with additional pathways affecting the plantar flexor muscles. The SCONE software, which provides optimisation tools for 2D neuromechanical simulation of human locomotion, is used to optimise the corresponding reflex parameters and simulate healthy gait. We then modelled various bilateral plantar flexor biomechanical and neural impairments, and individually introduced them in the healthy model. We characterised the resulting simulated gaits as pathological or not by comparing ankle kinematics and ankle moment with the healthy optimised gait based on metrics used in clinical studies. Our simulations suggest that toe walking can be generated by hyperreflexia, whereas muscle and neural weaknesses partially induce heel gait. Thus, this 'what if' approach is deemed of great interest as it allows investigation of the effect of various impairments on gait and suggests an important contribution of active reflex mechanisms to pathological toe gait. Key points: Pathological toe and heel gaits are commonly present in various conditions such as spinal cord injury, stroke and cerebral palsy.These conditions present various neural and biomechanical impairments, but the cause–effect relationships between these impairments and pathological gaits are difficult to establish clinically.Based on neuromechanical simulation, this study focuses on the plantar flexor muscles and builds a new reflex circuit controller to model and evaluate the potential effect of both neural and biomechanical impairments on gait.Our results suggest an important contribution of active reflex mechanisms to pathological toe gait.This 'what if' based on neuromechanical modelling is thus deemed of great interest to target potential causes of pathological gait. [ABSTRACT FROM AUTHOR]

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis—PAFO). Several studies have forecasted healthy gait trajectories, but, to the best of our knowledge, none have forecasted gait trajectories of children with pathological gait yet. These exhibit higher inter- and intra-subject variability compared to typically developing gait of healthy subjects. Pathological trajectories represent the typical gait patterns that rehabilitative exoskeletons and actuated orthoses would target. In this study, we implemented two deep learning models, a Long-Term Short Memory (LSTM) and a Convolutional Neural Network (CNN), to forecast hip, knee, and ankle trajectories in terms of corresponding Euler angles in the pitch, roll, and yaw form for children with neurological disorders, up to 200 ms in the future. The deep learning models implemented in our study are trained on data (available online) from children with neurological disorders collected by Gillette Children's Speciality Healthcare over the years 1994–2017. The children's ages range from 4 to 19 years old and the majority of them had cerebral palsy (73%), while the rest were a combination of neurological, developmental, orthopaedic, and genetic disorders (27%). Data were recorded with a motion capture system (VICON) with a sampling frequency of 120 Hz while walking for 15 m. We investigated a total of 35 combinations of input and output time-frames, with window sizes for input vectors ranging from 50–1000 ms, and output vectors from 8.33–200 ms. Results show that LSTMs outperform CNNs, and the gap in performance becomes greater the larger the input and output window sizes are. The maximum difference between the Mean Absolute Errors (MAEs) of the CNN and LSTM networks was 0.91 degrees. Results also show that the input size has no significant influence on mean prediction errors when the output window is 50 ms or smaller. For output window sizes greater than 50 ms, the larger the input window, the lower the error. Overall, we obtained MAEs ranging from 0.095–2.531 degrees for the LSTM network, and from 0.129–2.840 degrees for the CNN. This study establishes the feasibility of forecasting pathological gait trajectories of children which could be integrated with exoskeleton control systems and experimentally explores the characteristics of such intelligent systems under varying input and output window time-frames. [ABSTRACT FROM AUTHOR]

Objectives: To clarify a characteristic of dynamic stability during gait in idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD), and to explore the association between dynamic stability and disease severity in each disease. Materials and Methods: The 5‐m gait of 36 iNPH (precerebrospinal fluid drainage), 20 PD (medicated state), and 25 healthy controls (HC) were evaluated using three‐dimensional motion analysis. Ambulatory dynamic stability was defined as the ability to maintain the extrapolated center of mass within the base of support at heel contact, with the distance between the two referred to as the margin of stability (MOS). Results: Anteroposterior direction (AP) MOS was significantly larger in the iNPH and PD groups than in the HC group; no significant difference was found between the iNPH and PD groups. Mediolateral direction (ML) MOS was significantly larger in the iNPH and PD groups than in the HC group and significantly larger in the iNPH group than in the PD group. In the iNPH group, the disease severity was positively correlated with only ML MOS. In the PD group, the disease severity was positively correlated with the AP MOS and ML MOS. Conclusions: Dynamic stability in iNPH increases in AP and ML, and it may be associated with not only iNPH‐associated gait disturbance but also with a voluntarily cautious gait strategy. Dynamic stability in PD only increased in AP, and this may be associated with PD symptoms. These findings will help physicians understand the difference in pathological gait including dynamic stability between patients with iNPH and PD. [ABSTRACT FROM AUTHOR]

For interpreting outcomes of clinical gait analysis, an accurate estimation of gait events, such as initial contact (IC) and toe-off (TO), is essential. Numerous algorithms to automatically identify timing of gait events have been developed based on various marker set configurations as input. However, a systematic overview of the effect of the marker selection on the accuracy of estimating gait event timing is lacking. Therefore, we aim to evaluate (1) if the marker selection influences the accuracy of kinematic algorithms for estimating gait event timings and (2) what the best marker location is to ensure the highest event timing accuracy across various gait patterns. 104 individuals with cerebral palsy (16.0 ± 8.6 years) and 31 typically developing controls (age 20.6 ± 7.8) performed clinical gait analysis, and were divided into two out of eight groups based on the orientation of their foot, in sagittal and frontal plane at mid-stance. 3D marker trajectories of 11 foot/ankle markers were used to estimate the gait event timings (IC, TO) using five commonly used kinematic algorithms. Heatmaps, for IC and TO timing per group were created showing the median detection error, compared to detection using vertical ground reaction forces, for each marker. Our findings indicate that median detection errors can be kept within 7 ms for IC and 13 ms for TO when optimizing the choice of marker and detection algorithm toward foot orientation in midstance. Our results highlight that the use of markers located on the midfoot is robust for detecting gait events across different gait patterns.

For interpreting outcomes of clinical gait analysis, an accurate estimation of gait events, such as initial contact (IC) and toe-off (TO), is essential. Numerous algorithms to automatically identify timing of gait events have been developed based on various marker set configurations as input. However, a systematic overview of the effect of the marker selection on the accuracy of estimating gait event timing is lacking. Therefore, we aim to evaluate (1) if the marker selection influences the accuracy of kinematic algorithms for estimating gait event timings and (2) what the best marker location is to ensure the highest event timing accuracy across various gait patterns. 104 individuals with cerebral palsy (16.0 ± 8.6 years) and 31 typically developing controls (age 20.6 ± 7.8) performed clinical gait analysis, and were divided into two out of eight groups based on the orientation of their foot, in sagittal and frontal plane at mid-stance. 3D marker trajectories of 11 foot/ankle markers were used to estimate the gait event timings (IC, TO) using five commonly used kinematic algorithms. Heatmaps, for IC and TO timing per group were created showing the median detection error, compared to detection using vertical ground reaction forces, for each marker. Our findings indicate that median detection errors can be kept within 7 ms for IC and 13 ms for TO when optimizing the choice of marker and detection algorithm toward foot orientation in midstance. Our results highlight that the use of markers located on the midfoot is robust for detecting gait events across different gait patterns. [ABSTRACT FROM AUTHOR]

New Findings: What is the topic of this review?This narrative review explores past and recent findings on the mechanical determinants of energy cost during human locomotion, obtained by using a mechanical approach based on König's theorem (Fenn's approach).What advances does it highlight?Developments in analytical methods and their applications allow a better understanding of the mechanical–bioenergetic interaction. Recent advances include the determination of 'frictional' internal work; the association between tendon work and apparent efficiency; a better understanding of the role of energy recovery and internal work in pathological gait (amputees, stroke and obesity); and a comprehensive analysis of human locomotion in (simulated) low gravity conditions. During locomotion, muscles use metabolic energy to produce mechanical work (in a more or less efficient way), and energetics and mechanics can be considered as two sides of the same coin, the latter being investigated to understand the former. A mechanical approach based on König's theorem (Fenn's approach) has proved to be a useful tool to elucidate the determinants of the energy cost of locomotion (e.g., the pendulum‐like model of walking and the bouncing model of running) and has resulted in many advances in this field. During the past 60 years, this approach has been refined and applied to explore the determinants of energy cost and efficiency in a variety of conditions (e.g., low gravity, unsteady speed). This narrative review aims to summarize current knowledge of the role that mechanical work has played in our understanding of energy cost to date, and to underline how recent developments in analytical methods and their applications in specific locomotion modalities (on a gradient, at low gravity and in unsteady conditions) and in pathological gaits (asymmetric gait pathologies, obese subjects and in the elderly) could continue to push this understanding further. The recent in vivo quantification of new aspects that should be included in the assessment of mechanical work (e.g., frictional internal work and elastic contribution) deserves future research that would improve our knowledge of the mechanical–bioenergetic interaction during human locomotion, as well as in sport science and space exploration. [ABSTRACT FROM AUTHOR]

The analysis of human gait is a potential diagnostic instrument for the early and timely identification of pathologies and disorders. It can also supply valuable data for the development of biomedical devices such as prostheses, orthoses, and rehabilitation systems. Although various research papers in the literature have used human gait analyses, few studies have focused on the biomechanical parameters used. This paper presents an extensive review and analysis of the main biomechanical parameters commonly used in the human gait study. The aim is to provide a practical guide to support and understand of the choices and selection of the most appropriate biomechanical parameters for gait analysis. A comprehensive search in scientific databases was conducted to identify, review and analyze the academic work related to human gait analysis. From this search, the main biomechanical parameters used in healthy and pathological gait studies were identified and analyzed. The results have revealed that the spatiotemporal and angular gait parameters are the most used in the assessment of healthy and pathological human gait. [ABSTRACT FROM AUTHOR]

Background: Gait impairments are among the most common and impactful symptoms of Parkinson's disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity.Objective: We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients.Methods: Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems.Results: Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion.Conclusions: We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials. [ABSTRACT FROM AUTHOR]

Background: The subjective evaluation of pathological gait exhibits a low inter-rater reliability. Therefore, we developed a three-dimensional acceleration of the trunk during walking to assess the pathological gait quantitatively.Methods: We evaluated 97 patients who underwent the cerebrospinal tap test and were diagnosed with idiopathic normal pressure hydrocephalus (iNPH) and 68 healthy elderlies. The gait features of all patients were evaluated and classified as one of the following: freezing of gait, wide-based gait, short-stepped gait, shuffling gait, instability, gait festination, difficulty in changing direction, and balance disorder in standing up. All gait features of 68 healthy elderlies were treated as normal. Trunk acceleration was recorded automatically by a smartphone placed on the umbilicus during a 15-foot walking test. Two novel indices were created. The first index was a trunk acceleration index, which was defined as (forward acceleration fluctuation) + (vertical acceleration fluctuation) – (lateral acceleration fluctuation) based on the multivariate logistics regression model, and the second index was created by multiplying the forward acceleration with the vertical acceleration. Additionally, 95% confidence ellipsoid volume of the three-dimensional accelerations was assessed.Results: Forward and vertical acceleration fluctuations were significantly associated with the probability of an iNPH-specific pathological gait. The trunk acceleration index demonstrated the strongest association with the probability of an iNPH-specific pathological gait. The areas under the receiver-operating characteristic curves for detecting 100% probability of an iNPH-specific pathological gait were 86.9% for forward acceleration fluctuation, 88.0% for vertical acceleration fluctuation, 82.8% for lateral acceleration fluctuation, 89.0% for trunk acceleration index, 88.8% for forward × vertical acceleration fluctuation, and 87.8% for 95% confidence ellipsoid volume of the three-dimensional accelerations.Conclusions: The probability of a pathological gait specific to iNPH is high at the trunk acceleration fluctuation, reduced in the forward and vertical directions, and increased in the lateral direction.

Background: The subjective evaluation of pathological gait exhibits a low inter-rater reliability. Therefore, we developed a three-dimensional acceleration of the trunk during walking to assess the pathological gait quantitatively. Methods: We evaluated 97 patients who underwent the cerebrospinal tap test and were diagnosed with idiopathic normal pressure hydrocephalus (iNPH) and 68 healthy elderlies. The gait features of all patients were evaluated and classified as one of the following: freezing of gait, wide-based gait, short-stepped gait, shuffling gait, instability, gait festination, difficulty in changing direction, and balance disorder in standing up. All gait features of 68 healthy elderlies were treated as normal. Trunk acceleration was recorded automatically by a smartphone placed on the umbilicus during a 15-foot walking test. Two novel indices were created. The first index was a trunk acceleration index, which was defined as (forward acceleration fluctuation) + (vertical acceleration fluctuation) – (lateral acceleration fluctuation) based on the multivariate logistics regression model, and the second index was created by multiplying the forward acceleration with the vertical acceleration. Additionally, 95% confidence ellipsoid volume of the three-dimensional accelerations was assessed. Results: Forward and vertical acceleration fluctuations were significantly associated with the probability of an iNPH-specific pathological gait. The trunk acceleration index demonstrated the strongest association with the probability of an iNPH-specific pathological gait. The areas under the receiver-operating characteristic curves for detecting 100% probability of an iNPH-specific pathological gait were 86.9% for forward acceleration fluctuation, 88.0% for vertical acceleration fluctuation, 82.8% for lateral acceleration fluctuation, 89.0% for trunk acceleration index, 88.8% for forward × vertical acceleration fluctuation, and 87.8% for 95% confidence ellipsoid volume of the three-dimensional accelerations. Conclusions: The probability of a pathological gait specific to iNPH is high at the trunk acceleration fluctuation, reduced in the forward and vertical directions, and increased in the lateral direction. [ABSTRACT FROM AUTHOR]

Purpose: Ambulation is an important objective for people with pathological gaits. Exoskeleton robots can assist these people to complete their activities of daily living. There are exoskeletons that have been presented in literature to assist the elderly and other pathological gait users. This article presents a review of the degree of support required in the elderly and neurological gait disorders found in the human population. This will help to advance the design of robot-assisted devices based on the needs of the end users. Methods: The articles included in this review are collected from different databases including Science Direct, Springer Link, Web of Science, Medline and PubMed and with the purpose to investigate the gait parameters of elderly and neurological patients. Studies were included after considering the full texts and only those which focus on spatiotemporal, kinematic and kinetic gait parameters were selected as they are most relevant to the scope of this review. A systematic review and meta-analysis were conducted. Results: The meta-analysis report on the spatiotemporal, kinematic and kinetic gait parameters of elderly and neurological patients revealed a significant difference based on the type and level of impairment. Healthy elderly population showed deviations in the gait parameters due to age, however, significant difference is observed in the gait parameters of the neurological patients. Conclusion: A level of agreement was observed in most of the studies however the review also noticed some controversies among different studies in the same group. The review on the spatiotemporal, kinematics and kinetic gait parameters will provide a summary of the fundamental needs of the users for the future design and development of robotic assistive devices. The support requirements provide the foundation for designing assistive devices. The findings will be crucial in defining the design criteria for robot assistive devices. [ABSTRACT FROM AUTHOR]

This paper describes a method to characterize gait pathologies like cerebral palsy using work, energy, and angular momentum. For a group of 24 children, 16 with spastic diplegic cerebral palsy and 8 typically developed, kinematic data were collected at the subjects self selected comfortable walking speed. From the kinematics, the work-internal, external, and whole body; energy-rotational and relative linear; and the angular momentum were calculated. Our findings suggest that internal work represents 53% and 40% respectively of the whole body work in gait for typically developed children and children with cerebral palsy. Analysis of the angular momentum of the whole body, and other subgroupings of body segments, revealed a relationship between increased angular momentum and increased internal work. This relationship allows one to use angular momentum to assist in determining the kinetics and kinematics of gait which contribute to increased internal work. Thus offering insight to interventions which can be applied to increase the efficiency of bipedal locomotion, by reducing internal work which has no direct contribution to center of mass motion, in both normal and pathologic populations. [ABSTRACT FROM AUTHOR]

Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was −3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000–3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population.

Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was −3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000–3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population. [ABSTRACT FROM AUTHOR]

Rigid foot modelling approaches are still widely used to assess ankle joint kinetics in clinical biomechanical research. Yet, studies on healthy subjects using multi-segment kinetic foot models indicated that one-segment kinetic foot models tend to overestimate ankle joint kinetic data. Our aim was to compare ankle joint kinetics computed with a one-segment versus a multi-segment kinetic foot model in both asymptomatic and pathological gait. We also assessed whether differences between models can lead to different interpretations in clinical decision-making. A two-factor repeated measure analysis of variance was performed to investigate differences in ankle joint kinetics, with the first factor being group effect (control vs. patients) and second factor being foot model effect (one-segment vs. multi-segment). Minimal detectable change was calculated to assess the clinical relevance of the observed differences in ankle joint kinetics. Ankle joint peak kinematic, angular velocity and kinetic variables were all significantly overestimated (P < 0.05) when computed with the one-segment kinetic foot model. Kinetic differences in peak plantarflexion angular velocity and peak power generation were higher than their MDC-values. Ankle joint kinetics are significantly overestimated when computed with a rigid foot modelling approach in both asymptomatic and pathological gait. This overestimation leads to clinical misinterpretations as MDC-values were less than the observed overestimation. In future studies, it is of clinical relevance to assess ankle joint kinetics with a multi-segment foot modelling approach. • One-segment foot models overestimate ankle joint kinetics in pathological gait. • Overestimating ankle joint kinetics leads to clinically relevant misinterpretations. • Clinical motion analysis requires multi-segment foot modelling. [ABSTRACT FROM AUTHOR]

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

Objective: To explore differences in gait-specific long-term memory structures and actual gait performance between patients with hip osteoarthrosis, patients seen six months after total hip arthroplasty and healthy controls to gain insights into the role of the gait-specific mental representation for rehabilitation. Design: Cross-sectional study Subjects: Twenty hip osteoarthrosis patients, 20 patients seen six months after total hip arthroplasty and 20 healthy controls Methods: Spatio-temporal (gait speed, step length) and temporophasic (stance time, swing time, single support time, total double support time) gait parameters, and gait variability were measured with an electronic walkway (OptoGait). The gait-specific mental representation was assessed using the structural dimensional analysis of mental representations (SDA-M). Results: Hip osteoarthrosis patients showed significantly longer stance and total double support times, shorter swing and single support times, and a decreased gait speed as compared with healthy controls (all P < 0.01). The differences in double support times were still evident in patients seen six months after total hip arthroplasty (P < 0.01). The gait-specific mental representation differed between hip osteoarthrosis patients and healthy controls with regard to mid-stance and mid-swing phases; the mid-stance phase was still affected six months after total hip arthroplasty (both P < 0.05). Conclusion: Our data indicated that actual gait performance and gait-specific long-term memory structures differ between hip osteoarthrosis patients and healthy controls. Important, some of these disease-related changes were still evident in patients seen six months after total hip arthroplasty. [ABSTRACT FROM AUTHOR]

Abstract Cerebral palsy (CP) is a neurological disorder that results in life-long mobility impairments. Musculoskeletal models used to investigate mobility deficits for children with CP often lack subject-specific characteristics such as altered muscle strength, despite a high prevalence of muscle weakness in this population. We hypothesized that incorporating subject-specific strength scaling within musculoskeletal models of children with CP would improve accuracy of muscle excitation predictions in walking simulations. Ten children (13.5 ± 3.3 years; GMFCS level II) with spastic CP participated in a gait analysis session where lower-limb kinematics, ground reaction forces, and bilateral electromyography (EMG) of five lower-limb muscles were collected. Isometric strength was measured for each child using handheld dynamometry. Three musculoskeletal models were generated for each child including a 'Default' model with the generic musculoskeletal model's muscle strength, a 'Uniform' model with muscle strength scaled allometrically, and a 'Custom' model with muscle strength scaled based on handheld dynamometry strength measures. Muscle-driven gait simulations were generated using each model for each child. Simulation accuracy was evaluated by comparing predicted muscle excitations and measured EMG signals, both in the duration of muscle activity and the root-mean-square difference (RMSD) between signals. Improved agreement with EMG were found in both the 'Custom' and 'Uniform' models compared to the 'Default' model indicated by improvement in RMSD summed across all muscles, as well as RMSD and duration of activity for individual muscles. Incorporating strength scaling into musculoskeletal models can improve the accuracy of walking simulations for children with CP. [ABSTRACT FROM AUTHOR]

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson’s disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians’ gait assessment and to monitor patients in their daily environment.

This paper integrates an unobtrusive and affordable sensing technology with machine learning methods to discriminate between healthy and pathological gait patterns as a result of stroke or acquired brain injury. A feature analysis is used to identify the role of each body part in separating pathological patterns from healthy patterns. Gait features, including the orientations of the hips and spine (trunk), shoulders and neck (upper limb), knees and ankles (lower limb), are calculated during walking based on Kinect skeletal tracking sequences. Sequences of these features during three types of walking conditions were examined: 1) walking at self-pace (WSP); 2) walking at distracted (WD); and 3) walking at fast pace (WFP). Two machine learning approaches, an instance-based discriminative classifier ( $k$ -nearest neighbor) and a dynamical generative classifier (using Gaussian Process Latent Variable Model), are examined to distinguish between healthy and pathological gaits. Nested cross validation is implemented to evaluate the performance of the two classifiers using three metrics: F1-score, macro-averaged error, and micro-averaged error. The discriminative model outperforms the generative model in terms of the F1-score (discriminative: WSP > 0.95, WD > 0.96, and WFP > 0.95 and generative: WSP > 0.87, WD > 0.85, and WFP > 0.68) and macro-averaged error (discriminative: WSP < 0.08, WD < 0.1, and WFP < 0.09 and generative: WSP < 0.11, WD < 0.12, and WFP < 0.14). The dynamical generative model on the other hand obtains better micro-averaged error (discriminative: WSP < 0.37, WD < 0.3, and WFP < 0.35 and generative: WSP < 0.15, WD < 0.2, and WFP < 0.2). The high-dimensional gait features are divided into five subsets: lower limb, upper limb, trunk, velocity, and acceleration. An instance-based feature analysis method (ReliefF) is used to assign weights to each subset of features according to its discriminatory power. The feature analysis establishes the most informative features (upper limb, lower limb, and trunk) for identifying pathological gait. [ABSTRACT FROM PUBLISHER]

Predicting the movements, Ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, Both to understand pathology, As well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem (OCP) is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, Muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution toward bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation. [ABSTRACT FROM AUTHOR]

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The gait analysis history brought a lot of methods and methodologies. It became a standard to collect data and create databases of human gait characteristics. One of the reasons is to derive standard deviations to be able compare patients’ data. The most laboratories create their own databases to ensure objectivity in evaluation of pathological gait cases. Acquired normative data of lower limb anatomical joint angles were compared with normative data of three different world laboratories in this study. Methods of statistical characteristics testing were used for their comparison. The results of these tests showed considerable differences at the significance level alpha=0.05. It is evident, that there is still no standard database to compare gait data acquired in different laboratories. The trends are to establish standard roles for data acquisition to ensure objective assessment of patients’ gait.