Wearable technologies最新文献

{"title":"Sensorless model-based tension control for a cable-driven exosuit.","authors":"Elena Bardi, Adrian Esser, Peter Wolf, Marta Gandolla, Emilia Ambrosini, Alessandra Pedrocchi, Robert Riener","doi":"10.1017/wtc.2024.21","DOIUrl":"10.1017/wtc.2024.21","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e22"},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Viscous damping of tremor using a wearable robot with an optimized mechanical metamaterial.","authors":"Suhas Raghavendra Kulkarni, Dino Accoto, Domenico Campolo","doi":"10.1017/wtc.2024.15","DOIUrl":"10.1017/wtc.2024.15","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e20"},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design, modeling, and preliminary evaluation of a simple wrist-hand stretching orthosis for neurologically impaired patients.","authors":"Elissa D Ledoux, Nithin S Kumar, Eric J Barth","doi":"10.1017/wtc.2024.22","DOIUrl":"10.1017/wtc.2024.22","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e19"},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can back exosuits simultaneously increase lifting endurance and reduce musculoskeletal disorder risk?","authors":"K M Rodzak, P R Slaughter, D N Wolf, C C Ice, S J Fine, K E Zelik","doi":"10.1017/wtc.2024.8","DOIUrl":"10.1017/wtc.2024.8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e17"},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.","authors":"Pooya Chanu Maibam, Dingyi Pei, Parthan Olikkal, Ramana Kumar Vinjamuri, Nayan M Kakoty","doi":"10.1017/wtc.2024.13","DOIUrl":"10.1017/wtc.2024.13","url":null,"abstract":"<p><p>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 <i>t</i>-test showed that synergistic features yielded significantly higher area under curve values (<i>p</i> < .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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e18"},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Center of mass acceleration during walking: comparison between IMU and camera-based motion capture methodologies.","authors":"Jasmine Y Liang, Li-Shan Chou","doi":"10.1017/wtc.2024.12","DOIUrl":"10.1017/wtc.2024.12","url":null,"abstract":"<p><p>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 (<i>r =</i> 0.58) and was strong between IMU and ML5 or between CoM and ML5 accelerations (<i>r =</i> 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.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e16"},"PeriodicalIF":3.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function.","authors":"María Alejandra Díaz, Sander De Bock, Philipp Beckerle, Jan Babič, Tom Verstraten, Kevin De Pauw","doi":"10.1017/wtc.2024.9","DOIUrl":"10.1017/wtc.2024.9","url":null,"abstract":"<p><p>Advancements in wearable robots aim to improve user motion, motor control, and overall experience by minimizing energetic cost (EC). However, EC is challenging to measure and it is typically indirectly estimated through respiratory gas analysis. This study introduces a novel EMG-based objective function that captures individuals' natural energetic expenditure during walking. The objective function combines information from electromyography (EMG) variables such as intensity and muscle synergies. First, we demonstrate the similarity of the proposed objective function, calculated offline, to the EC during walking. Second, we minimize and validate the EMG-based objective function using an online Bayesian optimization algorithm. The walking step frequency is chosen as the parameter to optimize in both offline and online approaches in order to simplify experiments and facilitate comparisons with related research. Compared to existing studies that use EC as the objective function, results demonstrated that the optimization of the presented objective function reduced the number of iterations and, when compared with gradient descent optimization strategies, also reduced convergence time. Moreover, the algorithm effectively converges toward an optimal step frequency near the user's preferred frequency, positively influencing EC reduction. The good correlation between the estimated objective function and measured EC highlights its consistency and reliability. Thus, the proposed objective function could potentially optimize lower limb exoskeleton assistance and improve user performance and human-robot interaction without the need for challenging respiratory gas measurements.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e15"},"PeriodicalIF":3.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks.","authors":"Michele Francesco Penna, Luca Giordano, Stefano Tortora, Davide Astarita, Lorenzo Amato, Filippo Dell'Agnello, Emanuele Menegatti, Emanuele Gruppioni, Nicola Vitiello, Simona Crea, Emilio Trigili","doi":"10.1017/wtc.2024.16","DOIUrl":"10.1017/wtc.2024.16","url":null,"abstract":"<p><p>This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder-elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user's movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e14"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke.","authors":"Donatella Simonetti, Maartje Hendriks, Bart Koopman, Noel Keijsers, Massimo Sartori","doi":"10.1017/wtc.2024.14","DOIUrl":"10.1017/wtc.2024.14","url":null,"abstract":"<p><p>Commonly, quantitative gait analysis post-stroke is performed in fully equipped laboratories housing costly technologies for quantitative evaluation of a patient's movement capacity. Combining such technologies with an electromyography (EMG)-driven musculoskeletal model can estimate muscle force properties non-invasively, offering clinicians insights into motor impairment mechanisms. However, lab-constrained areas and time-demanding sensor setup and data processing limit the practicality of these technologies in routine clinical care. We presented wearable technology featuring a multi-channel EMG-sensorized garment and an automated muscle localization technique. This allows unsupervised computation of muscle-specific activations, combined with five inertial measurement units (IMUs) for assessing joint kinematics and kinetics during various walking speeds. Finally, the wearable system was combined with a person-specific EMG-driven musculoskeletal model (referred to as human digital twins), enabling the quantitative assessment of movement capacity at a muscle-tendon level. This human digital twin facilitates the estimation of ankle dorsi-plantar flexion torque resulting from individual muscle-tendon forces. Results demonstrate the wearable technology's capability to extract joint kinematics and kinetics. When combined with EMG signals to drive a musculoskeletal model, it yields reasonable estimates of ankle dorsi-plantar flexion torques (<i>R</i> ² = 0.65 ± 0.21) across different walking speeds for post-stroke individuals. Notably, EMG signals revealing an individual's control strategy compensate for inaccuracies in IMU-derived kinetics and kinematics when input into a musculoskeletal model. Our proposed wearable technology holds promise for estimating muscle kinetics and resulting joint torque in time-limited and space-constrained environments. It represents a crucial step toward translating human movement biomechanics outside of controlled lab environments for effective motor impairment monitoring.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e13"},"PeriodicalIF":3.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design, modeling, and preliminary evaluation of a 3D-printed wrist-hand grasping orthosis for stroke survivors.","authors":"Elissa D Ledoux, Eric J Barth","doi":"10.1017/wtc.2024.18","DOIUrl":"10.1017/wtc.2024.18","url":null,"abstract":"<p><p>Stroke causes neurological and physical impairment in millions of people around the world every year. To better comprehend the upper-limb needs and challenges stroke survivors face and the issues associated with existing technology and formulate ideas for a technological solution, the authors conversed with 153 members of the ecosystem (60 neuro patients, 30 caregivers, and 63 medical providers). Patients fell into two populations depending on their upper-limb impairment: spastic (stiff, clenched hands) and flaccid (limp hands). For this work, the authors chose to focus on the second category and developed a set of design constraints based on the information collected through customer discovery. With these in mind, they designed and prototyped a 3D-printed powered wrist-hand grasping orthosis (exoskeleton) to aid in recovery. The orthosis is easily custom-sized based on two parameters and derived anatomical relationships. The researchers tested the prototype on a survivor of stroke and modeled the kinematic behavior of the orthosis with and without load. The prototype neared or exceeded the target design constraints and was able to grasp objects consistently and stably, as well as exercise the patients' hands. In particular, donning time was only 42 s, as compared to the next fastest time of 3 min reported in literature. This device has the potential for effective neurorehabilitation in a home setting, and it lays the foundation for clinical trials and further device development.</p>","PeriodicalId":75318,"journal":{"name":"Wearable technologies","volume":"5 ","pages":"e12"},"PeriodicalIF":3.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}