Search

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity ( R 2  > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Wearable lower-limb joint angle estimation using a reduced inertial measurement unit (IMU) sensor set could enable quick, economical sports injury risk assessment and motion capture; however the vast majority of existing research requires a full IMU set attached to every related body segment and is implemented in only a single movement, typically walking. We thus implemented 3-dimensional knee and hip angle estimation with a reduced IMU sensor set during yoga, golf, swimming (simulated lower body swimming in a seated posture), badminton, and dance movements. Additionally, current deep-learning models undergo an accuracy drop when tested with new and unseen activities, which necessitates collecting large amounts of data for the new activity. However, collecting large datasets for every new activity is time-consuming and expensive. Thus, a transfer learning (TL) approach with long short-term memory neural networks was proposed to enhance the model's generalization ability towards new activities while minimizing the need for a large new-activity dataset. This approach could transfer the generic knowledge acquired from training the model in the source-activity domain to the target-activity domain. The maximum improvement in estimation accuracy (RMSE) achieved by TL is 23.6 degrees for knee flexion/extension and 22.2 degrees for hip flexion/extension compared to without TL. These results extend the application of motion capture with reduced sensor configurations to a broader range of activities relevant to injury prevention and sports training. Moreover, they enhance the capacity of data-driven models in scenarios where acquiring a substantial amount of training data is challenging.

Gesture recognition is crucial for enhancing human-computer interaction and is particularly pivotal in rehabilitation contexts, aiding individuals recovering from physical impairments and significantly improving their mobility and interactive capabilities. However, current wearable hand gesture recognition approaches are often limited in detection performance, wearability, and generalization. We thus introduce EchoGest, a novel hand gesture recognition system based on soft, stretchable, transparent artificial skin with integrated ultrasonic waveguides. Our presented system is the first to use soft ultrasonic waveguides for hand gesture recognition. EcoflexTM 00-31 and EcoflexTM 00-45 Near ClearTM silicone elastomers were employed to fabricate the artificial skin and ultrasonic waveguides, while 0.1 mm diameter silver-plated copper wires connected the transducers in the waveguides to the electrical system. The wires are enclosed within an additional elastomer layer, achieving a sensing skin with a total thickness of around 500 μ m. Ten participants wore the EchoGest system and performed static hand gestures from two gesture sets: 8 daily life gestures and 10 American Sign Language (ASL) digits 0-9. Leave-One-Subject-Out Cross-Validation analysis demonstrated accuracies of 91.13% for daily life gestures and 88.5% for ASL gestures. The EchoGest system has significant potential in rehabilitation, particularly for tracking and evaluating hand mobility, which could substantially reduce the workload of therapists in both clinical and home-based settings. Integrating this technology could revolutionize hand gesture recognition applications, from real-time sign language translation to innovative rehabilitation techniques.

This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- and double-leg drop landings via wearable inertial measurement units (IMUs) and machine learning. A real-time, modular LSTM model with four sub-deep neural networks was developed to estimate vGRF and KEM. Sixteen subjects wore eight IMUs on the chest, waist, right and left thighs, shanks, and feet and performed drop landing trials. Ground embedded force plates and an optical motion capture system were used for model training and evaluation. During single-leg drop landings, accuracy for the vGRF and KEM estimation was R 2 = 0.88 ± 0.12 and R 2 = 0.84 ± 0.14, respectively, and during double-leg drop landings, accuracy for the vGRF and KEM estimation was R 2 = 0.85 ± 0.11 and R 2 = 0.84 ± 0.12, respectively. The best vGRF and KEM estimations of the model with the optimal LSTM unit number (130) require eight IMUs placed on the eight selected locations during single-leg drop landings. During double-leg drop landings, the best estimation on a leg only needs five IMUs placed on the chest, waist, and the leg's shank, thigh, and foot. The proposed modular LSTM-based model with optimally-configurable wearable IMUs can accurately estimate vGRF and KEM in real-time with relatively low computational cost during single- and double-leg drop landing tasks. This investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening and intervention training programs.

Foot progression angle gait (FPA) modification is an important part of rehabilitation for a variety of neuromuscular and musculoskeletal diseases. While wearable haptic biofeedback could enable FPA gait modification for more widespread use than traditional tethered, laboratory-based approaches, retention, and cognitive demand in FPA gait modification via wearable haptic biofeedback are currently unknown and may be important to real-life implementation. Thus, the purpose of this study was to assess the feasibility of wearable haptic biofeedback to assess short-term retention and cognitive demand during FPA gait modification. Ten healthy participants performed toe-in (target 10 degrees change in internal rotation) and toe-out (target 10 degrees change in external rotation) haptic gait training trials followed by short-term retention trials, and cognitive multitasking trials. Results showed that participants were able to initially respond to the wearable haptic feedback to modify their FPA to adopt the new toe-in (9.7 ± 0.8 degree change in internal rotation) and toe-out (8.9 ± 1.0 degree change in external rotation) gait patterns. Participants retained the modified gait pattern on average within 3.9 ± 3.6 deg of the final haptic gait training FPA values. Furthermore, cognitive multitasking did not influence short-term retention in that there were no differences in gait performance during retention trials with or without cognitive multitasking. These results demonstrate that wearable haptic biofeedback can be used to assess short-term retention and cognitive demand during FPA gait modification without the need for traditional, tethered systems.

The recent explosion of wearable electronics has led to widespread interest in harvesting human movement energy, particularly during walking, for clinical and health applications. However, the amount of energy available to harvest and the required metabolic rate for wearable energy harvesting varies across subjects. In this paper, we utilize custom energy harvesting sliding shoes to develop and evaluate multivariate linear regression models to predict metabolic rate and energy harvesting rate during overground walking outside of the lab. Subjects performed 200 m self-selected normal and fast walking trials on flat ground with custom sliding shoes. Metabolic rate was measured with a portable breathing analysis system and energy harvesting rate was measured directly from the generator on the custom sliding shoes. Model performance was determined by comparing the difference between actual and predicted metabolic and energy harvesting rates. Overall, predictive modeling closely matched the actual values, and there was no statistical difference between actual and predicted average metabolic rate or between actual and predicted average energy harvesting rate. Energy harvesting sliding shoes could potentially be used for a variety of wearable devices to reduce onboard energy storage, and these findings could serve to inform expected energy harvesting rates and associated required metabolic cost for a diverse array of medical and health applications.

Rapid development in wearable electronics and systems continues to impose challenges on portable energy storage sustained over time, and thus human energy harvesting is a potentially attractive means of sustainable, long-term energy. We introduce a novel 'controlled slip' energy harvesting approach for capturing energy during human locomotion. While slip is normally considered undesirable, controlled slip holds potential to enable a significant amount of energy harvesting during each step of human gait. Custom-designed 'controlled slip' energy harvesting shoes were fabricated by mounting a sliding plate and a generator with a one-way bearing to the sole of standard walking shoes, which induces controlled forward slip during early stance while energy is harvested. Fourteen healthy subjects performed treadmill walking trials with the 'controlled slip' energy harvesting shoes which generated average electrical power of 1.15-1.44 W at walking speeds of 2.9-4.3 km/h. Interestingly, without prompting, subjects chose to walk with the 'controlled slip' energy harvesting shoes in either one of two distinct ways: landing with the heel first (heel strikers) or landing with the toe first (toe strikers). While heel strikers and toe strikers exhibited similar electrical power output and hip flexion angle at initial foot contact, heel strikers had higher peak ankle power and lower knee flexion angle at initial foot contact than toe strikers. 'Controlled slip' energy harvesting could potentially generate electrical power for a broad spectrum of wearable devices.

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy., Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance., Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively., Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Resonant frequency skin stretch uses cyclic lateral skin stretches matching the skin's resonant frequency to create highly noticeable stimuli, signifying a new approach for wearable haptic stimulation. Four experiments were performed to explore biomechanical and perceptual aspects of resonant frequency skin stretch. In the first experiment, effective skin resonant frequencies were quantified at the forearm, shank, and foot. In the second experiment, perceived haptic stimuli were characterized for skin stretch actuations across a spectrum of frequencies. In the third experiment, perceived haptic stimuli were characterized by different actuator masses. In the fourth experiment, haptic classification ability was determined as subjects differentiated haptic stimulation cues while sitting, walking, and jogging. Results showed that subjects perceived stimulations at, above, and below the skin's resonant frequency differently: stimulations lower than the skin resonant frequency felt like distinct impacts, stimulations at the skin resonant frequency felt like cyclic skin stretches, and stimulations higher than the skin resonant frequency felt like standard vibrations. Subjects successfully classified stimulations while sitting, walking, and jogging, perceived haptic stimuli was affected by actuator mass, and classification accuracy decreased with increasing speed, especially for stimulations at the shank. This paper could facilitate more widespread use of wearable skin stretch. Potential applications include gaming, medical simulation, and surgical augmentation, and for training to reduce injury risk or improve sports performance.

This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on the modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of ten modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. The overall hand gesture recognition classification accuracy was 94%. Further analysis revealed that the most important sensor location was the underside of the wrist and that when reducing the sensor number to only five optimally placed sensors, classification accuracy was still 90%. For continuous finger angle estimation, aggregate R 2 values between actual and predicted angles were thumb: 0.81 ± 0.10, index finger: 0.85±0.06, middle finger: 0.77±0.08, ring finger: 0.77 ± 0.12, and pinkie finger: 0.75 ± 0.10, and the overall average was 0.79 ± 0.05. These results demonstrate that a modified barometric pressure wristband can be used to classify hand gestures and to estimate individual finger joint angles. This approach could serve to improve the clinical treatment for upper extremity deficiencies, such as for stroke rehabilitation, by providing objective patient motor control metrics to inform and aid physicians and therapists throughout the rehabilitation process.