The Myoshirt, an active exosuit, provides gravity compensation for the shoulders. This study evaluated the impact of the Myoshirt on range of motion (ROM), endurance, and activities of daily living (ADLs) performance through tests involving nine participants with varying levels of arm impairments and diverse pathologies. Optical motion capture was used to quantify ROM of the shoulder and elbow joints during isolated movements and functional tasks. Endurance was quantified through a timed isometric shoulder flexion task, and a battery of ADL tasks was used to measure the perceived support of the exosuit, along with changes in movement quality. Feedback and usability insights were gathered with surveys. The Myoshirt did not significantly improve ROM during isolated movements (shoulder flexion, shoulder abduction, and elbow flexion/extension), but during the reaching phase of a functional drinking task elbow extension increased significantly by 13.5% (t = 7.52, p = .002). Participants could also keep their arms elevated 78.7% longer (t = 1.942, p = .047). Patients also reported less perceived difficulty with ADLs while using the device, and a therapist reported improved execution quality. Participants who self-reported severe impairment levels tended to derive greater benefits compared to those with milder impairments. These findings highlight the potential of the Myoshirt as an assistive device, particularly for individuals with severe impairments, while emphasizing the need for further refinement.

Accurate estimation of finger joint stiffness is important in assessing the hand condition of stroke patients and developing effective rehabilitation plans. Recent technological advances have enabled the efficient performance of hand therapy and assessment by estimating joint stiffness using soft actuators. While joint modular soft actuators have enabled cost-effective and personalized stiffness estimation, existing approaches face limitations. A corrective approach based on an analytical model suffers from actuator–finger and inter-actuator interactions, particularly in multi-joint systems. In contrast, a data-driven approach struggles with generalization due to limited availability of labeled data. In this study, we proposed a method for energy conservation-based online tuning of the analytical model using an artificial neural network (ANN) to address these challenges. By analyzing each term in the analytical model, we identified causes of estimation error and introduced correction parameters that satisfy energy balance within the actuator–finger complex. The ANN enhances the analytical model’s adaptability to measurement data, thereby improving estimation accuracy. The results show that our method outperforms the conventional corrective approach and exhibits better generalization potential than the purely data-driven approach. In addition, the method also proved effective in estimating stiffness in human subjects, where errors tend to be larger than in prototype experiments. This study is an essential step toward the realization of personalized rehabilitation.

The adoption of upper limb myoelectric prosthesis is limited by the lack of closed control loop systems. Although the efferent control has already been integrated into these devices, the sensory feedback restoration in the afferent channel still remains an open challenge. Transcutaneous electrical nerve stimulation (TENS) is a promising method for generating somatotopic sensory feedback, allowing the closure of the control loop system. The application of this technique is limited by cumbersome and grid-powered electrical stimulators, making them unsuitable for everyday life, whereas most portable stimulators available on the market are designed for other purposes (e.g., muscular stimulation or pain therapy) and present limited stimulation wave customization. The stimulation devices employed in the literature often produce not fully suitable stimulation parameters and are frequently validated through procedures that do not fully clarify their practical application for sensory feedback restoration. The research aims to present a novel wearable TENS stimulation device (46 g, 62 × 49 × 20 mm) suitable for sensory feedback application. The validation was achieved through a benchtop test and a preliminary analysis on 10 healthy participants comparing the qualities, intensities, and stimulated areas of the sensations elicited by the proposed device and a reference stimulator. The proposed device is capable of delivering charge-balanced stimulation waves over skin-like resistive load and eliciting tingling and vibration sensations with similar intensities compared to the adopted reference.

Cable-driven exoskeletons have recently shown great promise in the rehabilitation of stroke survivors. Numerical modeling/simulation provides a cost- and time-effective approach to fine-tuning design parameters of the exoskeletons, hence reducing the need for expensive and time-consuming experimental trials. This study investigated using a cable-driven lower limb rehabilitation exoskeleton (C-LREX) to correct stroke-impaired gait and track reference healthy trajectories. The impact of different levels of impairment and subject anthropometry variation on the model’s performance was studied. The C-LREX model was successful in assisting the impaired limb to track the reference trajectory in all impaired gait patterns, except for higher impairment levels (>20° range of motion deviation at the hip joint). Subject anthropometry variation did not affect trajectory tracking when the cable routing was scaled to fit the user’s anthropometry. This study confirmed that the C-LREX model could simulate various impaired lower limb gait patterns in the sagittal plane and determine the cable tension requirements needed to correct the impairment. Future work includes expanding the framework to incorporate frontal plane motion and to validate C-LREX performance in assisting biplanar impaired motion.

Lightweight, adjustable, and affordable devices are needed to enable the next generation of effective, wearable adjuncts for rehabilitation. Used at home or in a rehabilitation setting, these devices have the potential to reduce compound pressures on hospitals and social care systems. Despite recent developments in soft wearable robots, many of these devices restrict the range of motion and lack quantitative assessment of moment transfer to the wearer. The decoupled design of our wearable device for upper-limb rehabilitation successfully delivers almost the full range of motion to the user, with a mean maximum flexion angle of 149° (SD = 8.5). In this article, for the first time, we show that in tests involving a wide range of participants, 82% of the moment produced by the actuator is applied to the wearer. This testing of elbow flexion moment transfer supports the effectiveness of the device. This research is a step toward effective pneumatic soft robotic wearable devices that are adaptable to a wide range of users – a necessary prerequisite for their widespread adoption in health care.

In the last two decades, the adoption of exoskeletal devices for the reduction of the biomechanical overload of workers has hugely increased. They allow relief of the biomechanical load of the operator and ensure the operator’s contact with the object without binding its interaction. In this work, the biomechanical and physiological effects on the user wearing upper limb passive exoskeletons have been evaluated to highlight the benefits and possible drawbacks introduced by their use in typical manufacturing tasks. MATE and PAEXO Shoulder passive exoskeletons have been assessed during the execution of different working gestures among static, dynamic, and quasi-static tasks on 16 healthy volunteers. The obtained results confirm that the adoption of such systems significantly impacts the users by reducing the muscular load, increasing endurance, and reducing the perceived effort. Moreover, this analysis pointed out the specific benefits introduced by one exoskeleton with respect to the other according to the specific task. The MATE has the potential to reduce muscle load during the execution of static tasks. Conversely, the PAEXO Shoulder positively impacts the users’ biomechanical performances in dynamic tasks.

Occupational shoulder exoskeletons can relieve workers during strenuous overhead work. Passive solutions are lightweight, robust, and cost-effective, but they can also restrict user movement, have limited support, and cannot dynamically adapt to different working conditions. Semi-active and active systems are still mostly the subject of research, and existing systems are heavy or have limited performance and support. Here, we present a lightweight semi-active exoskeleton for shoulder support that incorporates a novel motorized torque adjustment mechanism that varies the effective lever arm with which a spring applies force to the supporting joint. The mechanism is integrated into lateral structures and can be actuated via Bowden cables with motors located on the user’s back. The technical performance of the system was experimentally characterized in terms of its dynamic support torque profiles at six different support levels. Furthermore, adjustment times and energy consumption were investigated. The system showed plateau-like support torque profiles in the intended working range and could be adjusted from nearly 0 Nm up to 12 Nm of maximum support per arm. Adjustment times varied between 0.5 s for the adjustment of 20% of the total adjustment range and 1.0 s for a full activation/deactivation. Adjustments consumed between 0.1 As and 1.9 As of battery charge, allowing long operating times of up to one working day, using only a small 2 Ah battery. As a result, the exoskeleton provides high performance by combining comparatively high support, rapid motorized support adjustment, and low energy consumption in a lightweight design.

Exoskeletons that make running easier could increase users’ physical activity levels and provide related health benefits. In this paper, we present the design of a portable, powered ankle exoskeleton that assists running and uses lightweight and compact twisted string actuators. It has limited durability at this stage of development, but preliminary results of its power to mass density and potential for reducing the metabolic cost of running are promising. The exoskeleton can provide high peak power of 700 W per leg, 7 times more than prior twisted-string devices, and high peak torques of 43 Nm. Kinetostatic and dynamic models were used to select mass-optimal components, producing a device that weighs 1.8 kg per leg and 2.0 kg in a backpack. We performed preliminary tests on a single participant to evaluate the exoskeleton performance during both treadmill running and outdoor running. The exoskeleton reduced metabolic energy use by 10.8% during treadmill running tests and reduced cost of transport by 7.7% during outdoor running tests compared to running without the device. Unfortunately, the twisted string wore out quickly, lasting an average of 4 min 50 s before breaking. This exoskeleton shows promise for making running easier if string life challenges can be addressed.

This is a proof-of-concept study to compare the effects of a 2-week program of “Remind-to-move” (RTM) treatment using closed-loop and open-loop wearables for hemiparetic upper extremity in patients with chronic stroke in the community. The RTM open-loop wearable device has been proven in our previous studies to be useful to address the learned nonuse phenomenon of the hemiparetic upper extremity. A closed-loop RTM wearable device, which emits reminding cues according to actual arm use, was developed in this study. A convenience sample of 16 participants with chronic unilateral stroke recruited in the community was engaged in repetitive upper extremity task-specific practice for 2 weeks while wearing either a closed-loop or an open-loop ambulatory RTM wearable device on their affected hand for 3 hrs a day. Evaluations were conducted at pre-/post-intervention and follow-up after 4 weeks using upper extremity motor performance behavioral measures, actual arm use questionnaire, and the kinematic data obtained from the device. Results showed that both open-loop and closed-loop training groups achieved significant gains in all measures at posttest and follow-up evaluations. The closed-loop group showed a more significant improvement in movement frequency, hand functions, and actual arm use than did the open-loop group. Our findings supported the use of closed-loop wearables, which showed greater effects in terms of promoting the hand use of the hemiparetic upper extremity than open-loop wearables among patients with chronic stroke.

Recent advancements in wearable robots have focused on developing soft, compliant, and lightweight structures to provide comfort for the users and to achieve the primary function of assisting body motions. The interaction forces induced by physical human-robot interaction (pHRI) not only cause skin discomfort or pain due to relatively high localized pressures but also degrade the wearability and the safety of the wearer’s joints by unnaturally altering the joint reaction forces (JRFs) and the joint reaction moments (JRMs). Although the correlation between excessive JRFs/JRMs and joint-related conditions has been reported by researchers, the biomechanical effects of forces and moments caused by the pHRI of a wearable robot on the wearer’s joints remain under-analyzed. In this study, we propose a method of measuring and analyzing these interactions and effects, using a custom-designed soft, three-degree-of-freedom (DOF) force sensor. The sensor is made of four Hall effect sensors and a neodymium magnet embedded in a silicone elastomer structure, enabling simultaneous measurement of normal and two-axis shear forces by detecting the distance changes between the magnet and each Hall effect sensor. These sensors are embedded in contact pads of a commercial wearable robot and measure the interaction forces, used for calculating JRF and JRM. We also propose a modified inverse dynamics approach that allows us to consider the physical interactions between the robot and the human body. The proposed method of sensing and analysis provides the potential to enhance the design of future wearable robots, ensuring long-term safety.

Older adults often experience a decline in functional abilities, affecting their independence and mobility at home. Wearable lower-limb exoskeletons (LLEs) have the potential to serve as both assistive devices to support mobility and training tools to enhance physical capabilities. However, active end-user involvement is crucial to ensure LLEs align with users’ needs and preferences. This study employed a co-design methodology to explore home-based LLE requirements from the perspectives of older adults with mobility impairments and physiotherapists. Four older adults with self-reported mobility limitations participated by creating personas to represent different user needs and experiences (i.e., PERCEPT methodology), alongside four experienced physiotherapists who contributed their professional insights. As assistive devices, LLEs were seen as valuable for promoting independence, supporting mobility, and facilitating social participation, with essential activities including shopping, toileting, and outdoor walking. Physiotherapists expressed enthusiasm for integrating LLEs into remote rehabilitation programs, particularly to improve strength, balance, coordination, and walking speed. Key design considerations included a lightweight, discreet device that is easy to don and doff and comfortable for extended wear. Physiotherapists highlighted the potential of digital monitoring to assess physical parameters and personalize therapy. Fatigue emerged as a significant challenge for older adults, reinforcing the need for assistive LLEs to alleviate exhaustion and enhance functional independence. A shortlist of LLE features was drafted and scored, covering activity and design applications. These findings provide valuable insights into the design and usability of home-based LLEs, offering a foundation for developing devices that improve acceptance, usability, and long-term impact on healthy ageing.

Designing optimal assistive wearable devices is a complex task, often addressed using human-in-the-loop optimization and biomechanical modeling approaches. However, as the number of design parameters increases, the growing complexity and dimensionality of the design space make identifying optimal solutions more challenging. Predictive simulation, which models movement without relying on experimental data, provides a powerful tool for anticipating the effects of assistive devices on the human body and guiding the design process. This study aims to introduce a design optimization platform that leverages predictive simulation of movement to identify the optimal parameters for assistive wearable devices. The proposed approach is specifically capable of dealing with the challenges posed by high-dimensional design spaces. The proposed framework employs a two-layered optimization approach, with the inner loop solving the predictive simulation of movement and the outer loop identifying the optimal design parameters of the device. It is utilized for designing a knee exoskeleton with a damper to assist level-ground and downhill gait, achieving a significant reduction in normalized knee load peak value by $ 37\% $ for level-ground and by $ 53\% $ for downhill walking, along with a decrease in the cost of transport. The results indicate that the optimal device applies damping torques to the knee joint during the Stance phase of both movement scenarios, with different optimal damping coefficients. The optimization framework also demonstrates its capability to reliably and efficiently identify the optimal solution. It offers valuable insight for the initial design of assistive wearable devices and supports designers in efficiently determining the optimal parameter set.

Specialists globally employ various clinical scales and instruments to assess balance, gait, and motor functions in children with cerebral palsy (CP). Selecting appropriate assessment tools is essential for planning studies, developing effective treatment strategies, and tracking clinical outcomes. Given the diversity in assessment needs – whether evaluating dynamic, functional, or static balance – there is a need to identify the most suitable tools for each aspect. Therefore, the primary objective of this review is to critically analyze current clinical and instrument-based assessment methods in the literature to determine the most effective approaches for pediatric CP. This systematic review retrieved 1,812 papers, of which only 23 met the inclusion criteria and presented assessment methods for evaluating balance and motor functions in pediatric CP. These methods were further organized into clinical and instrument-based assessment groups. Among clinical examinations, the Pediatric Balance Scale and Gross Motor Function Measures were considered gold standards and featured in eight studies. In contrast, postural sway measured with the Biodex Balance System, Gait Stability Indices from the GAITRite system, and EMG sensing were the predominant instrument-based observations. Despite this variety, a consensus on the best assessment methods remains lacking. This review highlights the potential of integrating AI-driven metrics that combine clinical and instrument-based data to enhance precision and individualized care. Future research should focus on creating integrated, individualized profiles to better capture the unique capabilities of children with CP, enabling more personalized and effective intervention strategies.

This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $) and mean absolute error (MAE = $ {2.006}^{\circ } $), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ($ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ($ {r}^2>0.89 $). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.

Posture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess their posture. The Posture Lab focuses on two key aspects: Normal Head Posture (NHP) versus Forward Head Posture (FHP) measurement and thoracic spine kyphosis. Craniovertebral (CA) and Shoulder Angles (SA) quantify NHP and FHP. The Kyphosis Angle (KA) is for measuring normal thoracic spine and kyphosis. To measure these angles, the system uses computer vision technology with ArUco markers detection via a webcam to analyze head positions. Additionally, wearable accelerometer sensors measure kyphosis by checking the angles of inclination. The framework includes a web-based user interface for registration and specialized desktop applications for different measurement protocols. A RESTful API enables system communication and centralized data storage for reporting. The Posture Lab serves as an effective tool for organizations to evaluate employee postures and supports early intervention strategies, allowing timely referrals to healthcare providers if any potential musculoskeletal issues are identified. The Posture Lab has also shown medium to very high correlations with standard 2D motion analysis methods – Kinovea – for CA, SA, and KA in FHP with kyphosis measurements (r = 0.607, 0.704, and 0.992) and shown high to very high correlations in NHP with normal thoracic spine measurements (r = 0.809, 0.748, and 0.778), with significance at p < .01, utilizing the Pearson correlation coefficient.

Limb salvage surgery (LSS) with megaprosthesis is a common treatment for distal femur tumors, but its impact on gait remains poorly understood. Traditional gait analysis methods are costly and require specialized equipment. This study aims to compare spatiotemporal gait parameters between patients with distal femur megaprosthesis and healthy controls using an inertial measurement unit (IMU). We conducted a case–control study with 79 participants: 31 patients with distal femur megaprosthesis and 48 healthy controls. Gait data were collected using an IMU placed at L5-S1, capturing metrics such as gait quality index (GQI), pelvic kinematics, propulsion index, and gait speed. Statistical analysis included Student’s t-test, Mann–Whitney U test, and one-way ANOVA to compare gait parameters across groups. Patients with megaprosthesis exhibited significantly lower gait speed, propulsion index and anteroposterior acceleration symmetry index compared to controls (p < .05). GQI was reduced in the healthy legs of the cases (92.3%) compared to control legs (96.6%). Adaptations included prolonged stance phases in healthy legs and decreased single support phases in prosthetic legs. Despite these changes, gait patterns remained within functional ranges. IMU-based gait analysis reveals significant but functional alterations in gait mechanics among patients with distal femoral megaprosthesis. These findings underscore the need for tailored rehabilitation strategies to address compensatory mechanisms, optimize mobility, and enhance long-term outcomes. The use of IMU technology offers a cost-effective and portable alternative for clinical gait assessments.

The human need for rehabilitation, assistance, and augmentation has led to the development and use of wearable exoskeletons. Upper limb exoskeletons under research and development are tested on human volunteers to gauge performance and usability. Direct testing can often cause straining of the joints, especially the shoulder joint, which is the most important and flexible joint in the upper extremity of the human body. The misalignment of joint axes between the exoskeleton and the human body causes straining. To avoid this, we propose designing and developing a novel human shoulder phantom mimicking the shoulder complex motion and the humeral head translation that can help in the real-time testing of exoskeletons without the need for human volunteers. The device can be used to test the interaction forces and the maximum reachable position of the exoskeleton. It consists of three degrees of freedom (DOF) passive shoulder girdle mechanism and seven DOF glenohumeral joint mechanisms, of which six are passive revolute joints and one is an active prismatic joint mimicking the humeral head translation. All the passive joints are spring-loaded and are incorporated with joint angle sensors. A custom-made, three-axis force sensor measures the human–exoskeleton interaction forces. The design details, selection of joint springs, linear actuation mechanism, and the analysis of the phantom’s reachable workspace are presented. The device is validated by comparing the interaction forces produced during the conventional exoskeleton-assisted and human-assisted phantom arm elevation.

Upper-limb occupational exoskeletons reduce injuries during overhead work. Previous studies focused on muscle activation with and without exoskeletons, but their impact on shoulder fatigue remains unclear. Additionally, no studies have explored how exoskeleton support levels affect fatigue. This study investigates the effects of assistive profiles on muscular and cardiovascular fatigue. Electromyographic (EMG) and electrocardiographic signals were collected to compute EMG median frequency (MDF), heart rate (HR), and heart rate variability (HRV). Fatigue was assessed using three MDF and HR metrics: relative change ($ {\mathrm{MDF}}_{\Delta} $,$ {\mathrm{HR}}_{\Delta} $), slope ($ {\mathrm{MDF}}_{\mathrm{slope}} $,$ \mathrm{H}{\mathrm{R}}_{\mathrm{slope}} $), and intercept ($ {\mathrm{MDF}}_{\mathrm{intercept}} $,$ \mathrm{H}{\mathrm{R}}_{\mathrm{intercept}} $) of the linear regression. Results showed$ {\mathrm{MDF}}_{\Delta} $decreased 64% (p = 0.0020) with higher assistance compared to no exoskeleton; $ {\mathrm{HR}}_{\Delta} $ decreased 40% (p < 0.0273) with lower assistance, $ {\mathrm{MDF}}_{\mathrm{slope}} $ decreased up to 67% (p = 0.0039) and $ \mathrm{H}{\mathrm{R}}_{\mathrm{slope}} $ by 43% (p < 0.0098) with higher and medium assistance. HRV metrics included root mean square of successive differences (RMSSD) and low-frequency to high-frequency power ratio (LF/HF). RMSSD indicated parasympathetic dominance, while rising LF/HF ratio suggested physiological strain. Findings support occupational exoskeletons as ergonomic tools for reducing fatigue.