Search

Biomedical technologies are having an increasingly central role in the modern medicine. In fact they are at the root of the diagnosis and follow up of pathologies giving to the clinicians quantitative outcomes necessary on the choice of the right therapy. In this paper we will focus on biomedical technologies used in the context of gait analysis describing the main ones used in the clinical practice about pathologies of neurologic, orthopedic and rheumatic interest and underlining their importance in the clinical setting. The main systems for gait analysis will be presented in this article: system with passive markers, stereophotogrammetric system, force and pressure platforms, surface electromyography system, system based on inertial measurement units underling the importance of each in investigating a different aspect of movement and how integrating all of them we can have a depth and whole gait analysis. The main gait analysis protocols will be presented too. Finally, advantages and disadvantages about gait analysis will be analyzed. In conclusion, the complexity of the described biomedical technologies for gait analysis underlines the importance of the presence of an expert technician that can help the clinician to interpret and to process acquired signals during the gait analysis.

Heart rate is a nonstationary signal and its variation may contain indicators of current disease or warnings about impending cardiac diseases. Hence, heart rate variation analysis has become a noninvasive tool to further study the activities of the autonomic nervous system. In this scenario, the Poincaré plot analysis has proven to be a valuable tool to support cardiac diseases diagnosis. The study’s aim is a preliminary exploration of the feasibility of machine learning to classify subjects belonging to five cardiac states (healthy, hypertension, myocardial infarction, congestive heart failure and heart transplanted) using ten unconventional quantitative parameters extracted from bidimensional and three-dimensional Poincaré maps. Knime Analytic Platform was used to implement several machine learning algorithms: Gradient Boosting, Adaptive Boosting, k-Nearest Neighbor and Naïve Bayes. Accuracy, sensitivity and specificity were computed to assess the performances of the predictive models using the leave-one-out cross-validation. The Synthetic Minority Oversampling technique was previously performed for data augmentation considering the small size of the dataset and the number of features. A feature importance, ranked on the basis of the Information Gain values, was computed. Preliminarily, a univariate statistical analysis was performed through one-way Kruskal Wallis plus post-hoc for all the features. Machine learning analysis achieved interesting results in terms of evaluation metrics, such as demonstrated by Adaptive Boosting and k-Nearest Neighbor (accuracies greater than 90%). Gradient Boosting and k-Nearest Neighbor reached even 100% score in sensitivity and specificity, respectively. The most important features according to information gain are in line with the results obtained from the statistical analysis confirming their predictive power. The study shows the proposed combination of unconventional features extracted from Poincaré maps and well-known machine learning algorithms represents a valuable approach to automatically classify patients with different cardiac diseases. Future investigations on enriched datasets will further confirm the potential application of this methodology in diagnostic.

The use of e-textile technologies spread out in the scientific research with several applications in both medical and nonmedical world. In particular, wearable technologies and miniature electronics devices were implemented and tested for medical research purposes. In this paper, a systematic review regarding the use of e-textile for clinical applications was conducted: the Scopus and Pubmed databases were investigate by considering research studies from 2010 to 2020. Overall, 262 papers were found, and 71 of them were included in the systematic review. Of the included studies, 63.4% focused on information and communication technology studies, while the other 36.6% focused on industrial bioengineering applications. Overall, 56.3% of the research was published as an article, while the remainder were conference papers. Papers included in the review were grouped by main aim into cardiological, muscular, physical medicine and orthopaedic, respiratory, and miscellaneous applications. The systematic review showed that there are several types of applications regarding e-textile in medicine and several devices were implemented as well; nevertheless, there is still a lack of validation studies on larger cohorts of subjects since the majority of the research only focuses on developing and testing the new device without considering a further extended validation.

Hereditary Spastic Paraplegia (HSP) is a rare inherited neurological disorder, whose predominant feature is a spastic gait. Gait analysis represents an objective tool to quantify the impairment of gait pattern in patients with HSP, thus supporting diagnosis and monitoring of disease progression. This study contributes to the characterization of HSP pathological gait, providing the assessment of test-retest repeatability of 122 parameters regarding postural sway, anticipatory postural adjustment in step initiation, gait and turn tasks. Data are collected on a cohort of thirty-five HSP patients, performing three consecutive repetitions of the Instrumented Stand and Walk (iSAW) test provided by Mobility Lab gait analysis system by APDM. Intraclass Correlation Coefficient (ICC) is used to assess repeatability. Repeatability Limit (RL) has also been evaluated and compared to the absolute value of difference (DoM) of HSP patients' measurements mean and normative mean of the same variable, in order to understand which variable can better characterize HSP gait with respect to normal gait. Results show that gait and turn measurements are more repeatable than sway and anticipatory postural adjustments variables. Furthermore, this study confirms previous findings in this field, identifying, among other gait parameters, cadence, gait velocity, stride length and RoM of the shanks as the main distinctive parameters of the pathology. Conversely, the RoM of the knees presents excellent repeatability, but low difference between healthy and pathological subjects.

SWEET Sock is a wearable e-textile sock for gait analysis. It is based on the acquisition and digital processing of the angular velocities of the lower limbs. In this paper we focus on the study of test-retest reliability of this system in measuring spatio-temporal gait parameters. The analysis was simultaneously conducted on data acquired by a multicamera system for gait analysis (SMART-DX 700 by BTS), in order to have reference values. A group of healthy subjects, equipped with both systems, performed four repeated walking tests along an 11 m walkway, consecutively and under constant conditions. The four tests were repeated at preferred, slow and fast self-selected walking speed. The Intraclass Correlation Coefficient (ICC) and Minimum Detectable Change (MDC) were evaluated to assess the repeatability of the measures. ICC values range from moderate to excellent for all gait parameters assessed by smart socks. The novel system presents test-retest reliability values comparable to, if not higher than, those shown by the gold standard. Finally, the results of gait reliability as a function of walking speed show excellent ICCs and very low MDCs for all parameters evaluated on trials at fast velocity, supporting the referenced hypothesis that faster movement is more consistent.

Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision Tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. Tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores; the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.

Studies and reviews show that the vast majority of students around the world use heavy and uncomfortable backpacks, which could negatively affect their skeletal-muscle development or at least generate a non-physiological functional overload and a change in the kinematics of gait. The purpose of this study is to investigate the role of the school backpack during the execution of the Walk test trying to identify if and how much it affects walking in terms of space-time parameters considering whether it might be correlated to potential spine disorders during the development age. A population-based sample of 98 students (60% female) aging from 10 to 12 years old was studied; gender, age, weight and lower limb length were recorded. Kinematic data were computed using a wearable inertial device for gait analysis: G-WALK System by BTS Bioengineering and analyzed using t-test and Machine Learning. Overall, concerning t-test between free walk and walk with backpack, it emerges a significant statistical difference on 9 out of 10 kinematic parameters, of which 6 with maximum statistical significance (p-value < 0.0001). Machine Learning analysis was carried out through Linear Discriminant Analysis, Naive Bayes, AdaBoost and Random Forest algorithms considering as different classes: free walk and walk with backpack. Accuracy and ROC Area were considered as evaluation metrics. The best performances were reached with Linear Discriminant Analysis with an accuracy of 85.71% and a ROC Area of 0.92. Study results showed a drastic change on kinematic due to the backpack. These results should be taken in correct account to safeguard children’s health wearing backpack for prolonged periods.

Functional Electrical Stimulation (FES), is a tecnique that uses low-energy electrical pulses to artificially generate muscle contractions, in individuals with damages regarding the central nervous system. The application of FES in clinical environment involves both patients care and rehabilitation. Aim of this work is to introduce a clinical FES protocol for upper limbs rehabilitation, in order to assist and train the execution of complex movement, such as flexion- extension of wrist and fingers and palmar prehension. The new FES protocol has been tested on a cohort of five subjects with different upper limb neuromotor deficits, during their rehabilitation. The benefits deriving from the application of the new FES protocol have been evaluated by comparing specific quantitative electromyographic parameters assessed before and after the treatment. Results show effective improvements in performances of 4 patients out of 5.

Although a very large number of students in the world use uncomfortable and heavy backpacks, their negative influence on gait in terms of spatiotemporal and kinematic parameters are still not well investigated. The purpose of the paper is to investigate the role of the school backpack during the execution of the Timed Up and Go test trying to identify if and how much it affects walking in terms of spatiotemporal and kinematic parameters considering whether it might be correlated to low back pain in children. A population-based sample of 98 children students ages 10-12 years was studied; gender, age, weight and lower limb length were recorded. Data were computed using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering and analyzed through Inferential Statistics and Machine Learning. Overall, concerning Inferential Statistics carried out through ANOVA test for each motion parameter between free walk and walk with backpack, it was found that there is a significant statistical difference on 23 out of 30 motion parameters, of which 20 with maximum statistical significance (p

As an alternative to the traditional methods of analysis in the time and frequency domains regarding heart rate variability, new interest has been concentrated in using a non-linear analysis technique of the beat-beat time series, known as the Poincare Plot Analysis. The parameters provided by the analysis can be used as input for machine learning algorithms in order to distinguish patients in three classes of congestive heart failure, according to the New York Heart Association. Tree-based algorithms for classification and synthetic minority oversampling technique (SMOTE) for balancing the dataset with artificial data were implemented in Knime analytics platform, reaching an overall accuracy between 75% and 80%, specificity and sensitivity greater than 90% in some classes and F-measures ranging from 68% to 92%. Further investigations could be pursued with bigger datasets and avoiding the use of artificial data to balance the classes.

SWEET Sock is a wearable prototype device designed as a sensorized sock in E-Textile, which allows the acquisition of plantar pressure and acceleration signals deriving from the motion of the lower limbs. Aim of this study is to validate the device, comparing its collected data to the ones obtained by standard instrument used in clinical environment. Accelerometric signals recorded by Sweet Sock have been compared with those obtained by the inertial system Opal by APDM, while Zebris baropodometric platform by FDM has been used as the reference system to validate pressure signals. Bland-Altman analysis has been used to assess benchmarking. Both for accelerometric and pressure signals, no systematic or proportional difference between the devices has been carried out, resulting that reliable measures can be obtained with the prototype device.

Parkinson’s Disease is one of the most common neurodegenerative disorders. Its principal symptoms regard motor area, and gait is one of the most affected motor characteristics. In clinical environment Parkinsonian gait is often assessed through gait analysis. Opal System by APDM is a commercial device used to perform gait analysis with inertial measurements units (IMUs). In this study we evaluate repeatability of spatio-temporal gait measurements, assessed with Opal Instrumented Stand and Walk (ISAW) test on a cohort of forty-five Parkinsonian patients. Repeatability is assessed by means of Intraclass Correlation Coefficient (ICC) and Repeatability Limit (RL) for each variable. RL is then compared to the absolute value of difference (DoM) of PD patients’ measurements mean and normative mean of the same variable, in order to understand which variable can better characterize Parkinsonian gait with respect to normal gait. Results show that gait and turn measurements are more repeatable than sway and anticipatory postural adjustments variables, and they are proper indexes to better identify Parkinsonian walking features.

The paper presents a new e-textile-based system, named SWEET Shirt, for the remote monitoring of biomedical signals. The system includes a textile sensing shirt, an electronic unit for data transmission, a custom-made Android application for real-time signal visualisation and a software desktop for advanced digital signal processing. The device allows for the acquisition of electrocardiographic, bicep electromyographic and trunk acceleration signals. The sensors, electrodes, and bus structures are all integrated within the textile garment, without any discomfort for users. A wide-ranging set of algorithms for signal processing were also developed for use within the system, allowing clinicians to rapidly obtain a complete and schematic overview of a patient’s clinical status. The aim of this work was to present the design and development of the device and to provide a validation analysis of the electrocardiographic measurement and digital processing. The results demonstrate that the information contained in the signals recorded by the novel system is comparable to that obtained via a standard medical device commonly used in clinical environments. Similarly encouraging results were obtained in the comparison of the variables derived from the signal processing.

Despite the growing use of different wearable inertial systems for gait analysis in clinical setting, also based on a different sensor placement, there is still a lack of knowledge about the agreement between them and their repeatability. The purpose of this study is to investigate the agreement between two commercial wearable inertial systems for gait analysis: Opal and G-Walk Systems, and their repeatability. Fifty-three subjects, healthy and pathological, underwent a gait analysis session instrumented by both systems, seven spatiotemporal parameters were recorded. The study of agreement was carried out through Bland-Altman Analysis, Passing-Bablok regression and Paired t-test, the study of repeatability through the intra class correlation coefficient ICC(3,1). Study results showed a not perfect agreement between the two systems although they both showed good repeatability. This work underlines the importance to perform a study of agreement before using devices interchangeably or even as a replacement in order to have reliable measurements.