Wearable technologies最新文献

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.

We present a flexible, multilayer fabric strain sensor composed of a carbon fabric layer sandwiched between elastic bands. The sensor achieved a gauge factor of 3.4 and maintained its durability up to 635% strain. Its uniform graphite layer enabled reliable fabrication and easy integration into wearable formats. Performing well on commercial gloves and bands, the sensor effectively captured strain variations during body movement and enabled wireless transmission for real-time monitoring. Distinct resistance patterns were recorded for various body motions such as walking, jogging, jumping, and knee bending with a clear separation between high- and low-intensity activities. The overall design supports scalable fabrication and practical integration into wearable systems.

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 for level-ground and by 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.

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 = ) and mean absolute error (MAE = ), 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 ( of the joint range of motion [ROM]) and strong correlation coefficients (). 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.

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.

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.