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.

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.

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 ($ {\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.

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 250.8$ \pm $1.1 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.

The muscular restructuring and loss of function that occurs during a transfemoral amputation surgery has a great impact on the gait and mobility of the individual. The hip of the residual limb adopts a number of functional roles that would previously be controlled by lower joints. In the absence of active plantar flexors, swing initiation must be achieved through an increased hip flexion moment. The high activity of the residual limb is a major contributor to the discomfort and fatigue experienced by individuals with transfemoral amputations during walking. In other patient populations, both passive and active hip exosuits have been shown to positively affect gait mechanics. We believe an exosuit configured to aid with hip flexion could be well applied to individuals with transfemoral amputation. In this article, we model the effects of such a device during whole-body, subject-specific kinematic simulations of level ground walking. The device is simulated for 18 individuals of K2 and K3 Medicare functional classification levels. A user-specific device profile is generated via a three-axis moment-matching optimization using an interior-point algorithm. We employ two related cost functions that reflect an active and passive form of the device. We hypothesized that the optimal device configuration would be highly variable across subjects but that variance within mobility groups would be lower. From the results, we partially accept this hypothesis, as some parameters had high variance across subjects. However, variance did not consistently trend down when dividing into mobility groups, highlighting the need for user-specific design.

This article presents a bioinspired pneumatic soft actuator designed to mimic the flexo-extension movement of the human finger, with a particular focus on stiffness modulation through granular jamming. Three-chamber geometries – honeycomb, rectangular, and half-round – were evaluated to optimize curvature performance, utilizing Mold Star 15 Slow elastomer for actuator fabrication. Granular jamming, both passive and active, was implemented within the inextensible layer using chia and quinoa grains to enhance stiffness modulation. Experimental results revealed that the honeycomb geometry most closely aligned with the natural index finger trajectory. Stiffness evaluations demonstrated a range of 0–0.47 N/mm/° for quinoa and 0–0.9 N/mm/° for chia. The actuator’s force output increased by 16% for quinoa and 71% for chia compared to the nonjammed configuration. This enhanced performance is particularly beneficial for applications such as hand rehabilitation, where adaptive stiffness and force modulation are critical. Granular jamming, especially with active chia, provided superior adaptability for tasks requiring variable stiffness and resistance, making it a promising candidate for wearable robotic applications in rehabilitation.

This study introduces a novel method for gait analysis using a single inertial measurement unit placed on the sacrum. This method is valid not only on level ground but also on incline and decline conditions. The method leverages the “crackle” function, the third derivative of the sacral resultant acceleration, to identify right and left gait events. This approach is particularly effective in capturing the initial peak in acceleration data during foot impact with the ground, often overlooked by other methods. The study aimed to demonstrate the method’s accuracy in identifying the right- and left-side impacts during level ground, incline, and decline runs across a range of speeds. Additionally, the algorithm was applied in outdoor running scenarios, where it performed very well, further validating its robustness and reliability. The results are compared with other existing methods to highlight the effectiveness of this approach.

Eye gaze tracking is increasingly popular due to improved technology and availability. In the domain of assistive device control, however, eye gaze tracking is often used in discrete ways (e.g., activating buttons on a screen), and does not harness the full potential of the gaze signal. In this article, we present a method for collecting both reactionary and controlled eye gaze signals, via screen-based tasks designed to isolate various types of eye movements. The resulting data allows us to build an individualized characterization for eye gaze interface use. Results from a study conducted with participants with motor impairments are presented, offering insights into maximizing the potential of eye gaze for assistive device control. Importantly, we demonstrate the potential for incorporating direct continuous eye gaze inputs into gaze-based interface designs; generally seen as intractable due to the ‘Midas touch’ problem of differentiating between gaze movements for perception versus for interface operation. Our key insight is to make use of an individualized measure of smooth pursuit characteristics to differentiate between gaze for control and gaze for environment scanning. We also present results relating to gaze-based metrics for mental workload and show the potential for the concurrent use of eye gaze for control input as well as assessing a user’s mental workload both offline and in real-time. These findings might inform the development of continuous control paradigms using eye gaze, as well as the use of eye tracking as the sole input modality to systems that share control between human-generated and autonomy-generated inputs.

The wide adoption of occupational shoulder exoskeletons in industrial settings remains limited. Passive exoskeletons were proved effective in a limited amount of application scenarios, such as (quasi-)static overhead handling tasks. Quasi-active devices, albeit representing an improved version of their passive predecessors, do not allow full modulation of the amount of assistance delivered to the user, lacking versatility and adaptability in assisting various dynamic tasks. Active occupational shoulder exoskeletons could overcome these limitations by controlling the shape of the delivered torque profile according to the task they aim to assist. However, most existing active devices lack compactness and wearability. This prevents their implementation in working environments. In this work, we present a new active shoulder exoskeleton, named Active Exo4Work (AE4W). It features a new flexible shaft-driven remote actuation unit that allows the positioning of the motors close to the wearer’s center of mass while it maintains a kinematic structure that is compatible with the biological motion of the shoulder joint. in vitro and in vivo experiments have been conducted to investigate the performance of AE4W. Experimental results show that the exoskeleton is kinematically compatible with the user’s workspace since it does not constrain the natural range of motion of the shoulder joint. Moreover, this device can effectively provide different types of assistance while the user executes various dynamic tasks, without altering perceived comfort.

Machine – human interaction systems have been proposed to improve motion learning efficiency. We developed a pneumatic-driven motion teaching system that provides feedback to the learner by simultaneously presenting visual and torque information. We achieved a lightweight, soft, and user-safety haptic system using a pneumatic artificial muscle (PAM). The PAM’s shrink force was estimated based on its characteristic model and the suit link system, and the suit generated external torque. However, accurate force control was challenging due to the time delay of the feedback control, the loosening of the soft suit, and modeling errors of the driving PAM caused by hysteresis. To improve the force control performance of the motion teaching suit, this article’s contributions are to develop a novel suit in which PAMs for drive and force estimation are connected in series and implement a 2-degree-of-freedom (DOF) force control system using force estimation values in this suit and to confirm the effectiveness of the proposed hardware and software. This article contains three topics: (a) the development of novel suit hardware, (b) force estimation using a sealed small PAM, and (c) a proposal of force control using a 2-DOF controller. The effect of loosening the soft suit is reduced in the novel-developed suit. A sealed small PAM with small deformation and little hysteresis is adopted for force estimation. The time delay in feedback control is decreased by adopting the proposed novel 2-DOF control. Finally, the proposed suit and its control system were evaluated in experiments and achieved the desired performance.

Elastic textiles play a critical role in passive wearable solutions for musculoskeletal load management in both passive exosuits and resistance clothing. These textiles, based on their ability to stretch and retract, can exhibit ambivalence in their load-modulating effects when used in occupational, rehabilitation, exercise, or everyday activity settings. While passive exosuits and resistance garments may appear similar in design, they have opposing goals: to reduce the musculoskeletal load in the case of exosuits and to increase it in the case of resistance clothing. Despite this intrinsic connection, these two approaches have not been extensively linked together. This review aims to fill this gap by examining the common and distinct principles of elastic textiles in passive exosuits and resistance clothing, shedding light on their interactions and the complex dynamics of musculoskeletal load systems. The effectiveness of different designs in passive exosuits that mimic musculoskeletal function and resistance clothing that increase the workload for strength training are critically reviewed. Current challenges in practical implementation and opportunities to improve critical issues, such as preload, thermal comfort, skin friction, and donning and doffing are also highlighted.

State-of-the-art controllers for active back exosuits rely on body kinematics and state machines. These controllers do not continuously target the lumbosacral compression forces or adapt to unknown external loads. The use of additional contact or load detection could make such controllers more adaptive; however, it can be impractical for daily use. Here, we developed a novel neuro-mechanical model-based controller (NMBC) that uses a personalized electromyography (EMG)-driven musculoskeletal (MSK) model to estimate lumbosacral joint loading. NMBC provided adaptive, subject- and load-specific assistive forces proportional to estimates of the active part of biological joint moments through a soft back support exosuit. Without a priori information, the maximum assistive forces of the cable were modulated across weights. Simultaneously, we applied a non-adaptive, kinematic-dependent, trunk inclination-based controller (TIBC). Both NMBC and TIBC reduced the mean and peak biomechanical metrics, although not all reductions were significant. TIBC did not modulate assistance across weights. NMBC showed larger reductions of mean than peak values, significant reductions during the erect stance and the cumulative compressive loads by 21% over multiple cycles in a cohort of 10 participants. Overall, NMBC targeted mean lumbosacral compressive forces during lifting without a priori information of the load being carried. This may facilitate the adoption of non-hindering wearable robotics in real-life scenarios. As NMBC is informed by an EMG-driven MSK model, it is possible to tune the timing of NMBC-generated torque commands to the exosuit (delaying or anticipating commands with respect to biological torques) to target further reduction of peak or mean compressive forces and muscle fatigue.

Research in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon unit (MTU) contractile speed properties, less attention has been given to the development of wearable robotic controllers that can steer MTU dynamics directly. Therefore, closed-loop control of MTU dynamics needs to be robust across fiber phenotypes, that is ranging from slow type I to fast type IIx in humans. The ability to perform closed-loop control the in-vivo dynamics of MTUs could lead to a new class of wearable robots that can provide precise support to targeted MTUs for preventing onset of injury or providing precision rehabilitation to selected damaged tissues. In this paper, we introduce a novel closed-loop control framework that utilizes nonlinear model predictive control to keep the peak Achilles tendon force within predetermined boundaries during diverse range of cyclic force production simulations in the human ankle plantarflexors. This control framework employs a computationally efficient model comprising a modified Hill-type MTU contraction dynamics component and a model of the ankle joint with parallel actuation. Results indicate that the closed-form muscle-actuation model’s computational time is in the order of microseconds and is robust to different muscle contraction velocity properties. Furthermore, the controller achieves tendon force control within a time frame below $ 18\mathrm{ms} $, aligning with the physiological electromechanical delay of the MTU and facilitating its potential for future real-world applications.