Search

Community ambulation is essential for maintaining a healthy lifestyle, but it poses significant challenges for individuals with limb loss due to complex task demands. In wearable robotics, particularly powered prostheses, there is a critical need to accurately estimate environmental context, such as walking speed and slope, to offer intuitive and seamless assistance during varied ambulation tasks. We developed a user-independent and multicontext, intent recognition system that was deployed in real-time on an Open Source Leg (OSL). We recruited 11 individuals with transfemoral amputation, with seven participants used for real-time validation. Our findings revealed two main conclusions: (1) the user-independent (IND) performance across speed and slope was not statistically different from user-dependent (DEP) models in real-time and did not degrade compared to its offline counterparts, and (2) IND walking speed estimates showed ∼0.09 m/s mean absolute error (MAE) and slope estimates showed ∼0.95 deg MAE across multicontext scenarios. Additionally, we provide an open-source dataset to facilitate further research in accurately estimating speed and slope using an IND approach in real-world walking tasks on a powered prosthesis. [ABSTRACT FROM AUTHOR]

Spinal cord injuries (SCI) substantially affect sensory, motor, and autonomous functions below the level of injury, reducing the independence and quality of life for affected individuals. Specifically, people with SCI between C5 and C7 cervical levels encounter limitations in voluntary finger and wrist flexion, reducing grasp capability. Compensatory strategies like tenodesis grasp, whereby wrist extension passively closes the fingers, remain; this is effective for small and light objects but insufficient for heavier ones. Typically, wearable assistive exoskeletons are designed to actuate a person’s fingers, however, such devices are sensitive to anatomical variability, such as hand size and joint contractures. The Dorsal Grasper is a wearable device designed to address this challenge by leveraging voluntary wrist extension and providing human-robot collaborative grasping capabilities with underactuated supernumerary fingers on the back of the hand. In this study, we introduce kinematic assessment methods that we use to show how the Dorsal Grasper expands the graspable workspace and reduces trunk motion, especially in situations where the use of a wheelchair restricts the individual’s posture. Our functionally relevant experiments with multiple SCI participants demonstrate the Dorsal Grasper’s potential as a versatile assistive solution for enhancing grasping capability in individuals with distinct SCI profiles.

Assessment of occupational exoskeletons should ideally include longitudinal and multistage studies in real working scenarios to prove their effectiveness and sustainability in real in-field contexts and to help generalize the findings for specific scenarios. This work presents a comprehensive assessment methodology implemented as a multistage experimental campaign for rail industry workers using a back-support exoskeleton (StreamEXO). This work demonstrates that a sector/task-specific exoskeleton developed to address work task-specific requirements generates beneficial performance and user experience results. The experimental work in this paper involves collecting data from nine workers over multiple days of testing. During this testing, workers did not report hindrances to their work operations, with an acceptance rate of 86%. In addition, worker fatigue was reduced by 16.9% as measured through metabolic consumption, and 51% when assessed by perceived effort. This work supports the hypothesis that sector/task-specific exoskeletons when tailored to meet the needs of workers and the work tasks can produce demonstrable benefits in real industrial sectors.

Accurately identifying user needs in terms of assist timing and magnitude presents challenges for wearable power‐assist limb devices. Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. Physical reservoir computing (PRC), which utilizes the inherent nonlinearity of physical systems for data processing, offers a promising alternative. This study proposes a novel self‐estimated physical reservoir computing (SEPRC) model that improves traditional PRC models for gait perception using a wearable pneumatic physical reservoir. A core feature of the new model is the self‐estimation structure, wherein the outputs of the physical reservoir are mutually estimated. Experimental evaluations indicate that the SEPRC model outperforms traditional PRC in clustering time‐series reservoir output sequences with the same dimensionality. This enhanced clustering performance is subsequently leveraged in gait perception by incorporating Takagi–Sugeno fuzzy logic for joint angle estimation and a softmax activation function for walking condition recognition. The newly proposed time‐sequence processing approach facilitates the traditional PRC model to achieve higher accuracy in gait perception and greater robustness against the user's walking pattern variations while preserving PRC's hardware simplicity.

This paper conducts a thorough literature review and assessment of prevailing upper-limb rehabilitation devices, scrutinizing their strengths and limitations. The focus of this work is mainly on soft exosuit devices but some rigid and hybrid exoskeleton devices are also discussed as a comparative mean. Subsequently, this manuscript delineates explicit design guidelines with the intent of fostering a systematic approach toward innovation in the realm of upper-limb rehabilitation technology. Through an examination of current concepts and technological paradigms, this study seeks to contribute nuanced insights aimed at optimizing both efficacy and user experience in rehabilitation device design. The culmination of this critical analysis results in the proposal of a systematic design procedure to inform and influence the trajectory of specific user-tailored innovations within the domain of upper-limb rehabilitation devices.The proposed approach enables the identification of features and weaknesses in existing devices, facilitating also the design of innovative solutions for unsolved issues in the field of wearable robotics. A design example is presented to clarify the proposed design procedure. • A systematic review of the state of the art on wearable device. • Classification of wearable robots for upper limb motion assistance in ADLs, based on the number and the types of physiological DoFs. • An innovative and systematic method to guide designers in the initial phase of design of the wearable robots. • Case study description for method demonstration and assessment. [ABSTRACT FROM AUTHOR]

With increasing age, motor performance declines. This decline is associated with less favorable health outcomes such as impaired activities of daily living, reduced quality of life, or increased mortality. Through regular assessment of motor performance, changes over time can be monitored, and targeted therapeutic programs and interventions may be informed. This can ensure better individualization of any intervention approach (e.g. by considering the current motor performance status of a person) and thus potentially increase its effectiveness with regard to maintaining current performance status or delaying further decline. However, in older adults, motor performance assessment is time consuming and requires experienced examiners and specific equipment, amongst others. This is particularly not feasible in care facility/nursing home settings. Wearable robotic devices, such as exoskeletons, have the potential of being used to assess motor performance and provide assistance during physical activities and exercise training for older adults or individuals with mobility impairments, thereby potentially enhancing motor performance. In this manuscript, we aim to (1) provide a brief overview of age-related changes of motor performance, (2) summarize established clinical and laboratory test procedures for the assessment of motor performance, (3) discuss the possibilities of translating established test procedures into exoskeleton-based procedures, and (4) highlight the feasibility, technological requirements and prerequisites for the assessment of human motor performance using lower limb exoskeletons., (Creative Commons Attribution license.)

Background and Purpose: Throwing a baseball involves intense exposure of the arm to high speeds and powerful forces, which contributes to an increasing prevalence of arm injuries among athletes. Traditional rigid exoskeletons and rehabilitation equipment frequently lack portability, safety, ergonomic design, and affordability. Traditional rehabilitation approaches frequently require therapist monitoring, resulting in therapy delays. This paper describes a Pneumatic Artificial Muscle (PAM) based soft robotic sleeve designed for upper limb rehabilitation, with an emphasis on elbow flexion and extension in baseball pitchers., Methods: The experiment included five healthy male baseball pitchers aged 20-24 with no history of upper limb injuries, ensuring their suitability for testing the effectiveness of the PAM-based sleeve in supporting elbow flexion and extension specific to baseball pitching. The effectiveness of the PAM-based soft sleeve was evaluated utilizing a pre-post design that included surface electromyography (sEMG). Participants were tested for muscle activation and range of motion both with and without the device. The sleeve has two operational modes: Active Surge Flex Mode, which detects movement intention using sEMG and delivers targeted help, and Steady Passive Mode, which allows repetitive flexion and extension without intentional muscle activation., Results: The PAM-based sleeve reduced muscle activation in both triceps and biceps, with reductions of approximately 40% (p < 0.05) and 60% (p < 0.05), respectively. When activated based on assistance thresholds, triceps and biceps EMG amplitudes were further reduced by 30% (p < 0.05) and 50% (p < 0.05), respectively. The flexion angle remained consistent between 70 and 90°, indicating smooth and controlled movements. The Co-Contraction Index (CCI) decreased from 0.31 to 0.12, highlighting a reduction in unnecessary muscle co-activation, thereby improving movement efficiency., Discussion: The PAM-based soft robotic sleeve provides a convenient and effective upper limb rehabilitation solution that is specifically designed for baseball pitchers. The sleeve reduces the physical strain of pitchers by reducing muscle activation in both the biceps and triceps, as well as decreasing the Co-Contraction Index (CCI). Its portability, convenience of use, and potential to improve muscle efficiency make it an effective tool for improving rehabilitation outcomes., (© 2025 John Wiley & Sons Ltd.)