Wearable technologies最新文献

This paper explores the integration of haptic gloves and virtual reality (VR) environments to enhance industrial training and operational efficiency within the framework of Industry 4.0 and Industry 5.0. It examines the alignment of these technologies with the Sustainable Development Goals (SDGs), mainly focusing on SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). By incorporating a human-centric approach, the study leverages haptic gloves to provide realistic feedback and immersive experiences in virtual training environments. The gloves enable intuitive interaction, enhancing the training efficacy and reducing real-world operational errors. Using the 5S principles-Social, Sustainable, Sensing, Smart, and Safe-this research evaluates the system's impact across various dimensions. The findings indicate significant improvements in user comfort, productivity, and overall well-being, alongside enhanced sustainability and operational efficiency. However, challenges related to realistic hand-object interactions and algorithmic enhancements were identified. The study underscores the importance of continuous improvement and cross-disciplinary collaboration to advance the usability and effectiveness of these technologies. Future research should focus on customization, AI-driven adaptability, sustainability, real-world scalability, and comprehensive impact assessment to further develop smart interfaces in industrial settings. This integration represents a transformative opportunity to enhance workplace safety, skills development, and contribute to global sustainable development goals.

While active back-support exoskeletons can reduce mechanical loading of the spine, current designs include only one pair of actuated hip joints combined with a rigid structure between the pelvis and trunk attachments, restricting lumbar flexion and consequently intended lifting behavior. This study presents a novel active exoskeleton including actuated lumbar and hip joints as well as subject-specific exoskeleton control based on a real-time active low-back moment estimation. We evaluated the effect of exoskeleton support with different lumbar-to-hip (L/H) support ratios on spine loading, lumbar kinematics, and back muscle electromyography (EMG). Eight healthy males lifted 15 kg loads using three techniques without exoskeleton (NOEXO) and with exoskeleton: minimal impedance mode (MINIMP), L/H support ratio in line with a typical L/H net moment ratio (R0.8), lower (R0.5) and higher (R2.0) L/H support ratio than R0.8, and a mechanically fixed lumbar joint (LF; simulating hip joint-only exoskeleton designs). EMG-driven musculoskeletal model results indicated that R0.8 and R0.5 yielded significant reductions in spinal loading (4-11%, p < .004) across techniques when compared to MINIMP, through reducing active moments (14-30%) while not affecting lumbar flexion and passive moments. R2.0 and LF significantly reduced spinal loading (8-17%, p < .001; 22-26%, p < .001, respectively), however significantly restricted lumbar flexion (3-18%, 24-27%, respectively) and the associated passive moments. An L/H support ratio in line with a typical L/H net moment ratio reduces spinal loading, while allowing normal lifting behavior. High L/H support ratios (e.g., in hip joint-only exoskeleton designs) yield reductions in spinal loading, however, restrict lifting behavior, typically perceived as hindrance.

Internal and external rotation of the shoulder is often challenging to quantify in the clinic. Existing technologies, such as motion capture, can be expensive or require significant time to setup, collect data, and process and analyze the data. Other methods may rely on surveys or analog tools, which are subject to interpretation. The current study evaluates a novel, engineered, wearable sensor system for improved internal and external shoulder rotation monitoring, and applies it in healthy individuals. Using the design principles of the Japanese art of kirigami (folding and cutting of paper to design 3D shapes), the sensor platform conforms to the shape of the shoulder with four on-board strain gauges to measure movement. Our objective was to examine how well this kirigami-inspired shoulder patch could identify differences in shoulder kinematics between internal and external rotation as individuals moved their humerus through movement patterns defined by Codman's paradox. Seventeen participants donned the sensor while the strain gauges measured skin deformation patterns during the participants' movement. One-dimensional statistical parametric mapping explored differences in strain voltage between the rotations. The sensor detected distinct differences between the internal and external shoulder rotation movements. Three of the four strain gauges detected significant temporal differences between internal and external rotation (all p < .047), particularly for the strain gauges placed distal or posterior to the acromion. These results are clinically significant, as they suggest a new class of wearable sensors conforming to the shoulder can measure differences in skin surface deformation corresponding to the underlying humerus rotation.

Neuromuscular controllers (NMCs) offer a promising approach to adaptive and task-invariant control of exoskeletons for walking assistance, leveraging the bioinspired models based on the peripheral nervous system. This article expands on our previous development of a novel structure for NMCs with modifications to the virtual muscle model and reflex modulation strategy. The modifications consist firstly of simplifications to the Hill-type virtual muscle model, resulting in a more straightforward formulation and reduced number of parameters; and second, using a finer division of gait subphases in the reflex modulation state machine, allowing for a higher degree of control over the shape of the assistive profile. Based on the proposed general structure, we present two controller variants for hip exoskeletons, with four- and five-state reflex modulations (NMC-4 and NMC-5). We used an iterative data-driven approach with two tuning stages (i.e., muscle parameters and reflex gains) to determine the controller parameters. Biological joint torque profiles and optimal torque profiles for metabolic cost reduction were used as references for the final tuning outcome. Experimental testing under various walking conditions demonstrated the capability of both variants for adapting to the locomotion task with minimal parameter adjustments, mostly in terms of timing. Furthermore, NMC-5 exhibited better alignment with biological and optimised torque profiles in terms of timing characteristics and relative magnitudes, resulting in less negative mechanical work. These findings firstly validate the adequacy of the simplified muscle model for assistive controllers, and demonstrate the utility of a more nuanced reflex modulation in improving the assistance quality.

Cable-driven exosuits have the potential to support individuals with motor disabilities across the continuum of care. When supporting a limb with a cable, force sensors are often used to measure tension. However, force sensors add cost, complexity, and distal components. This paper presents a design and control approach to remove the force sensor from an upper limb cable-driven exosuit. A mechanical design for the exosuit was developed to maximize passive transparency. Then, a data-driven friction identification was conducted on a mannequin test bench to design a model-based tension controller. Seventeen healthy participants raised and lowered their right arms to evaluate tension tracking, movement quality, and muscular effort. Questionnaires on discomfort, physical exertion, and fatigue were collected. The proposed strategy allowed tracking the desired assistive torque with a root mean square error of 0.71 Nm (18%) at 50% gravity support. During the raising phase, the electromyography signals of the anterior deltoid, trapezius, and pectoralis major were reduced on average compared to the no-suit condition by 30, 38, and 38%, respectively. The posterior deltoid activity was increased by 32% during lowering. Position tracking was not significantly altered, whereas movement smoothness significantly decreased. This work demonstrates the feasibility and effectiveness of removing the force sensor from a cable-driven exosuit. A significant increase in discomfort in the lower neck and right shoulder indicated that the ergonomics of the suit could be improved. Overall this work paves the way toward simpler and more affordable exosuits.

Pathological tremors can often be debilitating to activities of daily living and significantly affect the quality of life. Such tremulous movements are commonly observed in wrist flexion-extension (FE). To suppress this tremor we present a wearable robot (WR) with a customized mechanical metamaterial (MM) as the physical human-robot interface (pHRI). The MM is optimized to conform to the user's wrist posture and follow the hand's Cartesian trajectory. This is done to minimize the shear between the pHRI and the user's skin and consequently improve wearability. This WR is then used to effect a viscous tremor suppression using the velocity of the user's wrist FE. We present a model for the interaction between the WR and the user with which we develop the viscous damping approach for tremor. This is then evaluated in simulation and using a dedicated test bed. This tremor suppression approach demonstrates an attenuation of 20-30 dB at various tremulous frequencies resulting in significantly lower tremor amplitudes due to the viscous damping.

This work studies upper-limb impairment resulting from stroke or traumatic brain injury and presents a simple technological solution for a subset of patients: a soft, active stretching aid for at-home use. To better understand the issues associated with existing associated rehabilitation devices, customer discovery conversations were conducted with 153 people in the healthcare ecosystem (60 patients, 30 caregivers, and 63 medical providers). These patients fell into two populations: spastic (stiff, clenched hands) and flaccid (limp hands). Focusing on the first category, a set of design constraints was developed based on the information collected from the customer discovery. With these constraints in mind, a powered wrist-hand stretching orthosis (exoskeleton) was designed and prototyped as a preclinical study (T0 basic science research) to aid in recovery. The orthosis was tested on two patients for proof-of-concept, one survivor of stroke and one of traumatic brain injury. The prototype was able to consistently open both patients' hands. A mathematical model was developed to characterize joint stiffness based on experimental testing. Donning and doffing times for the prototype averaged 76 and 12.5 s, respectively, for each subject unassisted. This compared favorably to times shown in the literature. This device benefits from simple construction and low-cost materials and is envisioned to become a therapy device accessible to patients in the home. This work lays the foundation for phase 1 clinical trials and further device development.

The objectives of this case series study were to test whether an elastic back exosuit could increase a wearer's endurance when lifting heavy objects and to assess whether lifting more cancels out the exosuit's risk reduction benefits. We found that 88% of participants increased their lifting repetitions while wearing an exosuit, with endurance increases ranging from 28 to 75%. We then used these empirical data with an ergonomic assessment model based on fatigue failure principles to estimate the effects on cumulative back damage (an indicator of low back disorder risk) when an exosuit is worn and more lifts are performed. Participants exhibited 27-93% lower cumulative back damage when wearing an exosuit. These results confirmed that wearing an exosuit increased participants' lifting capacity without canceling out injury risk reduction benefits. Back exosuits may make it possible to simultaneously boost productivity and reduce musculoskeletal disorder risks, which is relevant to workers in civilian and defense sectors.

Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.

Placing an inertial measurement unit (IMU) at the 5th lumbar vertebra (L5) is a frequently employed method to assess the whole-body center of mass (CoM) motion during walking. However, such a fixed position approach does not account for instantaneous changes in body segment positions that change the CoM. Therefore, this study aimed to assess the congruence between CoM accelerations obtained from these two methods. The CoM positions were calculated based on trajectory data from 49 markers placed on bony landmarks, and its accelerations were computed using the finite-difference algorithm. Concurrently, accelerations were obtained with an IMU placed at L5, a proxy CoM position. Data were collected from 16 participants. Bland-Altman Limits of Agreement and Statistical Parametric Mapping approaches were used to examine the similarity and differences between accelerations directly obtained from the IMU and those derived from position data of the L5 marker (ML5) and whole-body CoM during a gait cycle. The correlation was moderate between IMU and CoM accelerations (r = 0.58) and was strong between IMU and ML5 or between CoM and ML5 accelerations (r = 0.76). There were significant differences in magnitudes between CoM and ML5 and between CoM and IMU accelerations along the anteroposterior and mediolateral directions during the early loading response, mid-stance, and terminal stance to pre-swing. Such comprehensive understanding of the similarity or discrepancy between CoM accelerations acquired by a single IMU and a camera-based motion capture system could further improve the development of wearable sensor technology for human movement analysis.