Wearable technologies最新文献

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.

Our study investigated the efficacy and feasibility of screen-printed and ink-printed textile-based dry electrodes for electromyography (EMG) acquisition, marking a novel step in wearable telehealth (TH) system integration. We controlled the design and fabrication conditions of these textile EMG sensors, including electrode area and sizing, ensuring optimal contact pressure. Skin-electrode impedance for all designs was evaluated, and a 20 mm electrode diameter was deemed material-efficient and design-effective. When compared with standard 20 mm wet electrodes, our EMG sensors with the screen and inkjet-printed dry electrodes exhibited comparable signal-to-noise ratios (SNR_dB) to the conventional wet electrode (26 dB) with a peak of 25 dB, and 23 dB, respectively, emphasizing their reliability. Our research identified a 10% optimal strain by sizing for EMG performance across both printing techniques. These revelations support the future design of dependable, reusable dry textile electrodes, addressing challenges faced by wet electrodes. Additionally, the developed dry electrodes, when equipped with a Bluetooth-enabled amplifier puck mitigate common EMG challenges such as motion artifacts while promoting user comfort, which leads to an elevated user experience during EMG biosignal collection. The integration of the developed garment-based electrodes with available commercial technologies holds promise for enhancing TH systems and user engagement in wearable health monitoring.

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.

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 (,), slope (,), and intercept (,) of the linear regression. Results showed decreased 64% (p = 0.0020) with higher assistance compared to no exoskeleton; decreased 40% (p < 0.0273) with lower assistance, decreased up to 67% (p = 0.0039) and 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.

The human hand is an intricate anatomical structure essential for daily activities, yet replicating its full functionality in upper-limb prostheses remains a significant challenge. Despite advances in mechanical design leading to more sophisticated and dexterous artificial hands, difficulties persist in effectively controlling these prostheses due to the limitations posed by the muscle conditions of their users. These constraints result in a limited number of control inputs and a lack of sensory feedback. To address these issues, various semi-autonomous control strategies have been proposed, which integrate sensing technologies to complement traditional myoelectric control. Inspired by human grasping physiology, we propose a shared control strategy that divides grasp control into two levels: a high-level controller, operated by the user to initiate the grasp action, and a low-level controller, which ensures stability throughout the task. This work focuses specifically on slip detection methods, introducing improvements to the low-level controller to enable more autonomous grasping behavior during object holding. The proposed slip module uses distributed 3D force sensors across the artificial hand and integrates a friction cone strategy to ensure an appropriate shear-to-normal force ratio with bandpass filtering for establishing an initial stable grasp model without prior knowledge. Experimental evaluations consist of the comparison of this novel controller with conventional state-of-the-art approaches. Results demonstrate its efficacy in preventing slippage while requiring less grasping force than previous methods. Additionally, a qualitative validation was conducted to assess its responsiveness compared to human grasping reactions to unexpected weight changes, yielding positive outcomes.

Spurred by the global pandemic, research in health monitoring has pivoted towards the development of smart garments, enabling long-term tracking of individuals' cardiovascular health by continuously monitoring the electrocardiogram (ECG) and detecting any abnormality in the signal morphology. Many types of dry electrodes have been proposed as alternatives to gold standard Ag/AgCl wet electrodes, and they have been integrated into clothes capable of acquiring only a limited number of the different ECG traces. This limitation severely diminishes the diagnostic utility of the collected ECG data and obstructs the garment's potential for clinical-level evaluation. Here, we demonstrate a special ECG upper armband with a glove component which houses graphene-textile electrodes, where a fully mobile, exploring electrode located at the index finger enables the user to strategically position the electrode on-demand to desired body areas and measure the different ECG traces that are bipolar limb and unipolar chest leads. Based on measurements with and without employing the well-known Wilson Central Terminal (WCT) arrangement, the correlation ratio of unipolar ECG chest leads acquired with the graphene textile-based armband and Ag/AgCl electrodes both in "WCT-less" configuration reach up to %99.65; and up to %99.54 when Ag/AgCl electrodes are utilized "with WCT" while the graphene-based armband in "WCT-less" configuration. To the authors' best knowledge, this study reports the first multilead on-demand "touch-and-measure" ECG recording from a fully wearable textile garment. Moreover, owing to the human-centered armband design, we achieved a more than three-fold reduction in electrode count from 10 in clinical ECG practice down to 3.

The integration of wearable smart garments with multiple sensors has gained momentum, enabling real-time monitoring of users' vital parameters across various domains. This study presents the development and validation of an instrumented smart shirt for risk prevention in workplaces designed to enhance worker safety and well-being in occupational settings. The proposed smart shirt is equipped with sensors for collecting electrocardiogram, respiratory waveform, and acceleration data, with signal conditioning electronics and Bluetooth transmission to the mobile application. The mobile application sends the data to the cloud platform for subsequent Preventive Risk Index (PRI) extraction. The proposed SenseRisc system was validated with eight healthy participants during the execution of different physically exerting activities to assess the capability of the system to capture physiological parameters and estimate the PRI of the worker, and user subjective perception of the instrumented intelligent shirt.

Adjusting the assistive torque of upper limb occupational exoskeletons is essential to optimize their effectiveness and user acceptance in companies. This adjustment enables a balance to be struck between the expected benefits and potential undesirable effects associated with their use, particularly for the shoulder joint, which is sensitive to the balance of forces. Despite this, no study has yet evaluated these assistive torques in static and dynamic conditions representative of work situations. The aim of this article is therefore to evaluate these assistive torques under these two conditions, using an isokinetic dynamometer. Angular velocities ranging from 0 to 240°/s and four levels of assistance were investigated. The results showed that the maximum assistive torques in flexion (energy restitution phase) were lower than those in extension (tensioning phase) by 20 to 36% and were median in static conditions. It was also observed that the level of assistance and the exoskeleton opening angles had a strong impact on the assistive torques, unlike the angular velocity in dynamic conditions, which had a minimal effect. Quantifying these assistive torques is crucial for assessing their biomechanical impact and adjusting the exoskeleton's assistance to the operator and the task performed.

Electromyogram (EMG)-controlled prosthetic hands have advanced significantly during the past two decades. However, most of the currently available prosthetic hands fail to replicate human hand functionality and controllability. To measure the emulation of the human hand by a prosthetic hand, it is important to evaluate the functional characteristics. Moreover, incorporating feedback from end users during clinical testing is crucial for the precise assessment of a prosthetic hand. The work reported in this manuscript unfolds the functional characteristics of an EMG-CoNtrolled PRosthetIC Hand called ENRICH. ENRICH is a real-time EMG controlled prosthetic hand that can grasp objects in 250.81.1 ms, fulfilling the neuromuscular constraint of a human hand. ENRICH is evaluated in comparison to 26 laboratory prototypes and 10 commercial variants of prosthetic hands. The hand was evaluated in terms of size, weight, operation time, weight lifting capacity, finger joint range of motion, control strategy, degrees of freedom, grasp force, and clinical testing. The box and block test and pick and place test showed ENRICH's functionality and controllability. The functional evaluation reveals that ENRICH has the potential to restore functionality to hand amputees, improving their quality of life.

Wearable exoskeletons hold the potential to provide valuable physical assistance across a range of tasks, with applications steadily expanding across different scenarios. However, the lack of universally accepted testbeds and standardized protocols limits the systematic benchmarking of these devices. In response, the STEPbySTEP project, funded within the Eurobench framework, proposes a modular, sensorized, reconfigurable staircase testbed designed as a novel evaluation approach within the first European benchmarking infrastructure for robotics. This testbed, to be incorporated into the Eurobench testing facility, focuses on stairs as common yet challenging obstacles in daily life that provide a unique benchmark for exoskeleton assessment. The primary aim of STEPbySTEP is to propose a modular framework - including a specialized staircase design, tentative metrics, and testing protocols - to aid in evaluating and comparing exoskeleton performance. Here, we present the testbed and protocols developed and validated in preliminary trials using three exoskeletons: two lower-limb exoskeletons (LLEs) and one back-support exoskeleton. The results offer initial insights into the adaptability of the staircase testbed across devices, showcasing example metrics and protocols that underscore its benchmarking potential.