Improving Task-Agnostic Energy Shaping Control of Powered Exoskeletons with Task/Gait Classification.

Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users.

Step Width Estimation in Individuals With and Without Neurodegenerative Disease Via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors.

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs.

Real-Time IMU-Based Kinematics in the Presence of Wireless Data Drop.

Wireless inertial motion capture holds promise for real-time human-machine interfaces and home-based rehabilitation applications. However, wireless data drop can cause significant estimation errors deteriorating performance or even making the system unusable. It is currently unclear how to estimate non-periodic kinematics with wearable inertial measurement units (IMUs) in the presence of wireless data drop (packet loss).

EchoGest: Soft Ultrasonic Waveguides Based Sensing Skin for Subject-Independent Hand Gesture Recognition.

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.

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

Background: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures.

Automated, IMU-based spine angle estimation and IMU location identification for telerehabilitation.

Background: Telerehabilitation is a promising avenue for improving patient outcomes and expanding accessibility. However, there is currently no spine-related assessment for telerehabilitation that covers multiple exercises.

Methods: We propose a wearable system with two inertial measurement units (IMUs) to identify IMU locations and estimate spine angles for ten commonly prescribed spinal degeneration rehabilitation exercises (supine chin tuck head lift rotation, dead bug unilateral isometric hold, pilates saw, catcow full spine, wall angel, quadruped neck flexion/extension, adductor open book, side plank hip dip, bird dog hip spinal flexion, and windmill single leg).

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force.

3D Knee and Hip Angle Estimation With Reduced Wearable IMUs via Transfer Learning During Yoga, Golf, Swimming, Badminton, and Dance.

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.

IMU Shoulder Angle Estimation: Effects of Sensor-to-Segment Misalignment and Sensor Orientation Error.

Accurate shoulder joint angle estimation is crucial for analyzing joint kinematics and kinetics across a spectrum of movement applications including in athletic performance evaluation, injury prevention, and rehabilitation. However, accurate IMU-based shoulder angle estimation is challenging and the specific influence of key error factors on shoulder angle estimation is unclear. We thus propose an analytical model based on quaternions and rotation vectors that decouples and quantifies the effects of two key error factors, namely sensor-to-segment misalignment and sensor orientation estimation error, on shoulder joint rotation error.

Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides.

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).

Synthetic EMG Based on Adversarial Style Transfer Can Effectively Attack Biometric-Based Personal Identification Models.

Biometric-based personal identification models are generally considered to be accurate and secure because biological signals are too complex and person-specific to be fabricated, and EMG signals, in particular, have been used as biological identification tokens due to their high dimension and non-linearity. We investigate the possibility of effectively attacking EMG-based identification models with adversarial biological input via a novel EMG signal individual-style transformer based on a generative adversarial network and tiny leaked data segments. Since two same EMG segments do not exist in nature; the leaked data can't be used to attack the model directly or it will be easily detected.

Challenges and advances in the use of wearable sensors for lower extremity biomechanics.

The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events.

Real-Time Ground Reaction Force and Knee Extension Moment Estimation During Drop Landings Via Modular LSTM Modeling and Wearable IMUs.

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.

Multi-day monitoring of foot progression angles during unsupervised, real-world walking in people with and without knee osteoarthritis.

Background: Foot progression angle is a biomechanical target in gait modification interventions for knee osteoarthritis. To date, it has only been evaluated within laboratory settings.

Methods: Adults with symptomatic knee osteoarthritis (n = 30) and healthy adults (n = 15) completed two conditions: 1) treadmill walking in the laboratory (5-min), and 2) real-world walking outside of the laboratory (1-week).

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation.

Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes.

A Novel PPG-FMG-ACC Wristband for Hand Gesture Recognition.

Wrist-based hand gesture recognition has the potential to unlock naturalistic human-computer interaction for a vast array of virtual and augmented reality applications. Photoplethysmography (PPG), force myography (FMG), and accelerometry (ACC) have generally been proposed as isolated single sensing modalities for gesture recognition, but any of these alone is inherently limited in the amount of biological information it can collect during finger and hand movements. We thus propose a novel, wrist-based, PPG-FMG-ACC combined sensing approach based on a multi-head attention mechanism fusion convolutional neural network (CNN-AF) for gesture recognition.

Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke.

Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke.

Reducing Slip Risk: A Feasibility Study of Gait Training with Semi-Real-Time Feedback of Foot-Floor Contact Angle.

Slip-induced falls, responsible for approximately 40% of falls, can lead to severe injuries and in extreme cases, death. A large foot-floor contact angle (FFCA) during the heel-strike event has been associated with an increased risk of slip-induced falls. The goals of this feasibility study were to design and assess a method for detecting FFCA and providing cues to the user to generate a compensatory FFCA response during a future heel-strike event.

Strike index estimation using a convolutional neural network with a single, shoe-mounted inertial sensor.

Strike index is a measurement of the center of pressure position relative to the foot length, and it is regarded as a gold standard in classifying strike pattern in runners. However, strike index requires sophisticated laboratory equipment, e.g.

Predicting knee adduction moment response to gait retraining with minimal clinical data.

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients.

Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation.

In this paper, we introduced a novel ankle band with a vibrational sensor that can achieve low-cost ankle flexion angle estimation, which can be potentially used for automated ankle flexion angle estimation in home-based foot drop rehabilitation scenarios. Previous ankle flexion angle estimation methods require either professional knowledge or specific equipment and lab environment, which is not feasible for foot drop patients to achieve accurate measurement by themselves in a home-based scenario. To solve the above problems, a prototype was developed based on the assumption that the echo of a vibration signal on the tibialis anterior had different acoustic impedance distribution.

Activities of Daily Living-Based Rehabilitation System for Arm and Hand Motor Function Retraining After Stroke.

Most stroke survivors have difficulties completing activities of daily living (ADLs) independently. However, few rehabilitation systems have focused on ADLs-related training for gross and fine motor function together. We propose an ADLs-based serious game rehabilitation system for the training of motor function and coordination of both arm and hand movement where the user performs corresponding ADLs movements to interact with the target in the serious game.