SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Enhancing walking efficiency of adolescents with neurological impairments using an exosuit for ambulatory activities of daily living.

Introduction: Children and adolescents with neurological impairments face reduced participation and independence in daily life activities due to walking difficulties. Existing assistive devices often offer insufficient support, potentially leading to wheelchair dependence and limiting physical activity and daily life engagement. Mobile wearable robots, such as exoskeletons and exosuits, have shown promise in supporting adults during activities of daily living but are underexplored for children. Methods: We conducted a cross-sectional study to examine the potential of a cable-driven exosuit, the Myosuit, to enhance walking efficiency in adolescents with diverse ambulatory impairments. Each participant walked a course including up-hill, down-hill, level ground walking, and stairs ascending and descending, with and without the exosuit's assistance. We monitored the time and step count to complete the course and the average heart rate and muscle activity. Additionally, we assessed the adolescents' perspective on the exosuit's utility using a visual analog scale. Results: Six adolescents completed the study. Although not statistically significant, five participants completed the course with the exosuit's assistance in reduced time (time reduction range: [-3.87, 17.42]%, p-value: 0.08, effect size: 0.88). The number of steps taken decreased significantly with the Myosuit's assistance (steps reduction range: [1.07, 15.71]%, p-value: 0.04, effect size: 0.90). Heart rate and muscle activity did not differ between Myosuit-assisted and unassisted conditions (p-value: 0.96 and 0.35, effect size: 0.02 and 0.42, respectively). Participants generally perceived reduced effort and increased safety with the Myosuit's assistance, especially during tasks involving concentric contractions (e.g., walking uphill). Three participants expressed a willingness to use the Myosuit in daily life, while the others found it heavy or too conspicuous. Discussion: Increased walking speed without increasing physical effort when performing activities of daily living could lead to higher levels of participation and increased functional independence. Despite perceiving the benefits introduced by the exosuit's assistance, adolescents reported the need for further modification of the device design before using it extensively at home and in the community.

Nocturnal vestibular stimulation using a rocking bed improves a severe sleep disorder in a patient with mitochondrial disease.

Mitochondrial diseases are rare genetic disorders often accompanied by severe sleep disorders. We present the case of a 12-year-old boy diagnosed with a severe primary mitochondrial disease, exhibiting ataxia, spasticity, progressive external ophthalmoplegia, cardiomyopathy and severely disrupted sleep, but no cognitive impairment. Interestingly, his parents reported improved sleep during night train rides. Based on this observation, we installed a rocking bed in the patient's bedroom and performed different interventions, including immersive multimodal vestibular, kinesthetic and auditory stimuli, reminiscent of the sensory experiences encountered during train rides. Over a 5-month period, we conducted four 2-week nocturnal interventions, separated by 1-week washout phases, to determine the subjectively best-perceived stimulation parameters, followed by a final 4-week intervention using the optimal parameters. We assessed sleep duration and quality using the Mini Sleep Questionnaire, monitored pulse rate changes and used videography to document nocturnal interactions between the patient and caregivers. Patient-reported outcome measures, clinical examinations and personal outcomes of specific interests were used to document daytime sleepiness, restlessness, anxiety, fatigue, cognitive performance and physical posture. In the final 4-week intervention, sleep duration increased by 25%, required caregiver interactions reduced by 75%, and caregiving time decreased by 40%. Subjective fatigue, assessed by the Checklist Individual Strength, decreased by 40%, falling below the threshold of severe fatigue. Our study suggests that rocking beds could provide a promising treatment regime for selected patients with persistent severe sleep disorders. Further research is required to validate these findings in larger patient populations with sleep disorders and other conditions.

Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor.

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its surface.We characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile sensor.We observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %.The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed setup.Clinical Relevance- Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today's gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is concealed under the mattress while classifying sleeping positions with high accuracy.

Gamification of Physical Therapy Exercises Using Commercial Entertainment Content: A Safety and Feasibility Study.

Independent physiotherapy at home is a crucial element of rehabilitative care for a wide range of conditions as it constitutes a large portion of the overall therapy dose. However, up to 80% of individuals who are prescribed at-home physiotherapy do not consistently adhere to their treatment schedule, resulting in poor treatment outcomes. This is likely due to a lack of motivation and progress tracking in the current standard of care. We have developed a novel software prototype that allows users to control commercial entertainment content, such as video games or interactive music videos, with their movements during physiotherapy. By connecting therapy to proven entertainment content, we aim to improve on the current motivational deficits. This study investigated the safety and feasibility of this concept in a controlled environment over four physical therapy sessions with seven patients suffering from musculoskeletal and neurological conditions. As a secondary outcome, patients were asked about their enjoyment, perceived competence and effort using the Intrinsic Motivation Inventory (IMI) questionnaire. All participants were able to interact with the presented entertainment content and completed the study with no adverse events. Despite the diversity in pathology, age and training scenarios, the entertainment content maintained the patients' enjoyment with a high average rate of 6/7 on the IMI scale. Interacting with commercial entertainment content by doing physical therapy exercises was feasible, safe, and well-received over the six-week study period.

Enhancing Sleep Quality with Closed-Loop Autotuning of a Robotic Bed.

Sleep is essential to boost the rehabilitation outcome as it facilitates motor learning, enhances cognitive performance, and improves mood and well-being. Rocking beds that provide vestibular stimulation may be a promising and non-invasive alternative to conventional pharmaceutical treatments for individuals with sleep problems, offering regenerative sleep without unwanted side effects. Previous research has shown that the effectiveness of the interventions is related to the chosen rocking acceleration. Moreover, the movement of the bed must be comfortable and smooth to avoid disturbing the user's sleep. Previously, the motor control parameters were tuned manually, which was time-consuming, subjective, and did not guarantee minimum deviation from the desired acceleration profile. In this work, we present an efficient and effective method using Gaussian processes to automatically tune the PI control parameters of a rocking bed moving along the longitudinal axis. We first simulated the kinematics of a rocking bed and optimized the control parameters for a chosen objective function that included the desired and the actual accelerations in the movement direction. We then compared the number of iterations needed to reach this objective for a model based on Gaussian processes and for a model based on a naive random exploration of the parameter space. Finally, we implemented the Gaussian process on the rocking bed to automatically tune the control parameters and subjectively compared them to the control parameters that were previously obtained after manual tuning. Our simulation showed that we can reach the control objective after a constant number of iterations using Gaussian processes, independent of the search space size. For the random search, the number of iterations increased quadratically with the size of the search space. The Gaussian process was found to be well transferable to the rocking bed. After less than one hour, control parameters were discovered that outperformed the previous parameters in terms of smoothness. However, despite the smoother motion, the noise emission from the motor, which was not part of the optimization, increased considerably. Our presented technique based on Gaussian processes significantly reduced the time and effort required to optimize the bed's control parameters compared to manual tuning. In future work, the control objective has to be refined to include noise emission as an optimization metric as low noise is an important aspect in sleep-related applications.

Transferability of a Sensing Mattress for Posture Classification from Research into Clinics.

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

Cooperative Goal Generation for Reaching Tasks in Robot-Assisted Rehabilitation.

Robot-assisted neurorehabilitation requires automated generation of goal positions for reaching tasks in functional movement therapy. In state-of-the-art solutions, these positions are determined by a motivational therapy game either through constraints on the end-effector (2D or 3D games), or individual arm joints (1D games). Consequently, these positions cannot be adapted to the patients' specific needs by the therapist, and the effectiveness of the training is reduced. We solve this issue by generating goal positions using Gaussian Mixture Models and probability density maps based on the active range of motion of the patient and desired activities, while being compliant with existing game constraints. Therapists can modify the goal generation via an intuitive difficulty and activity parameter. The pipeline was tested on the upper-limb exoskeleton ANYexo 2.0. We have shown that the range of motion exploration rate could be altered from 0.39% to 5.9% per task and that our method successfully generated a sequence of reaching tasks that matched the range of motion of the selected activity, up to an inlier accuracy of 78.9%. Results demonstrate that the responsibilities of the therapy game (i.e., motivating the patient) and the therapists (i.e., individualizing the training) could be distributed properly. We believe that with our pipeline, effective cooperation between the involved agents is achieved, and the provided therapy can be improved.

Learning Activities of Daily Living from Unobtrusive Multimodal Wearables: Towards Monitoring Outpatient Rehabilitation.

Monitoring activities of daily living (ADLs) for wheelchair users, particularly spinal cord injury individuals is important for understanding the rehabilitation progress, customizing treatment plans, and observing the onset of secondary health conditions. This work proposes an innovative sensory system for measuring and classifying ADLs relevant to secondary health conditions. We systematically evaluated multiple wearable sensors such as pressure distribution mats on the wheelchair seat, accelerometer data from the ear and wrists, and IMU data from the wheelchair wheels to achieve the best unobtrusive combination of sensors that successfully distinguished ADLs. Our work resulted in an XGBoost classifier with a 20-second window size and extracted features in statistical, time, frequency, and wavelet domains, with an average class-wise F1 score of 82% (with only 3 out of 12 classes being mislabeled). Our study results demonstrate that the newly investigated modality of the bottom pressure mat emerges as the most relevant information source for recognizing ADLs, while heart and respiratory rates did not provide added value for the selected set of ADLs. The proposed sensory system and methodology proved high quality in most classes and easily extendable for long-term monitoring in outpatient rehabilitation, with the need for an extended database of activities.

Sensory-Motor Neurostimulation to Enhance Exosuit Performance.

Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To overcome these limitations, we developed the REINFORCE system, that complements the mechanical assistance provided by an exosuit, the Myosuit, with (1) functional electrical stimulation to enhance the activities of leg muscles, and (2) transcutaneous electrical nerve stimulation to restore somatosensory information. It consists of a fully portable and highly modular system that can be easily adapted to the level of impairment and specific need of each participant. Technical verification with three healthy participants showed reliable synchronization between all modules of the systems in all phases of walking. Additionally, we tested the system's effectiveness in one participant with multiple sclerosis who walked overground with and without functional electrical stimulation. Results showed a slight increase in self-selected walking speed (approx. 18%) and in the peak hip flexion at late swing (approx. 12%) as well as reduced step-to-step variability of step length and step time when electrical stimulation was provided. Our findings push towards a clinical trial involving more patients to validate the effectiveness of the REINFORCE system on participants' mobility.

Guided Exploration Leads to Faster Familiarization with a Wearable Robot: First Results of an Innovative Protocol.

Wearable robots show promise in addressing physical and functional deficits in individuals with mobility impairments. However, the process of learning to use these devices can take a long time. In this study, we propose a novel protocol to support the familiarization process with a wearable robot (the Myosuit) and achieve faster walking speeds. The protocol involves applying an anterior pulling force while participants perform a series of 10-meter Walking Tests (10mWT) with or without the Myosuit under various experimental conditions. We hypothesized that guiding the exploration of novel walking patterns can help the users learn to exploit the Myosuit's assistance faster by leading to larger step lengths and ultimately higher walking speeds. In this paper, we present the preliminary results of the protocol with seven participants with lower-limb mobility impairments. Participants who were assisted by the Myosuit showed a continuous increase in walking speed over the course of the pulling part of the experiment with a maximum increase of 41.3% (10.4%) when compared to the baseline 10mWT. Following the removal of the pulling force, these participants continued to show an increased walking speed while being supported by the Myosuit. This higher walking speed was primarily due to a significant increase in step length of 24% (16.6%) and cadence of 11% (8.9%). The results of this study may help the development of familiarization techniques for wearable robots.

Use of a novel pressure distribution system for severely ill neonates: a clinical pilot study carried out by the PREPICare consortium.

BACKGROUND: Pressure Injuries are not exclusively an adult phenomenon; various risk factors contribute to a high prevalence rate of 43% in the neonatal and pediatric intensive care population. Effective preventive measures in this population are limited. METHODS: We performed a pilot study to analyze the distribution and localization of support surface interface pressures in neonates in a pediatric intensive care unit (PICU). The hypothesis was that pressure redistribution by a novel air mattress would reduce pressure peaks in critical neonates. The measurements were conducted in a 27-bed level III PICU between November and December 2020. This included measuring pressure distribution and pressure peaks for five neonates positioned on either a state-of-the-art foam mattress or a new prototype air mattress. RESULTS: We confirmed that the pressure peaks were significantly reduced using the prototype air mattress, compared with the state-of-the-art foam mattress. The reduction of mean pressure values was 9-29%, while the reduction of the highest 10% of pressure values was 23-41%. CONCLUSIONS: The journey to an effective, optimal, and approved product for severely ill neonates to reduce Pressure Injuries is challenging. However, a crucial step was completed by this pilot study with the first pressure measurements in a real-world setting and the successful realization of a decrease in pressure peaks obtained using a prototype air mattress.

Do robots outperform humans in human-centered domains?

The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, "How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g., locomotion, manipulation), and their underlying structures (e.g., actuators/muscles) in human-centered domains?" This is the most comprehensive comparison of the literature so far. Most state-of-the-art robotic structures required for visual, tactile, or vestibular perception outperform human structures at the cost of slightly higher mass and volume. Electromagnetic and fluidic actuation outperform human muscles w.r.t. speed, endurance, force density, and power density, excluding components for energy storage and conversion. Artificial joints and links can compete with the human skeleton. In contrast, the comparison of locomotion functions shows that robots are trailing behind in energy efficiency, operational time, and transportation costs. Robots are capable of obstacle negotiation, object manipulation, swimming, playing soccer, or vehicle operation. Despite the impressive advances of humanoid robots in the last two decades, current robots are not yet reaching the dexterity and versatility to cope with more complex manipulation and locomotion tasks (e.g., in confined spaces). We conclude that state-of-the-art humanoid robotics is far from matching the dexterity and versatility of human beings. Despite the outperforming technical structures, robot functions are inferior to human ones, even with tethered robots that could place heavy auxiliary components off-board. The persistent advances in robotics let us anticipate the diminishing of the gap.

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury.

BACKGROUND: Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability. METHODS: Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test-retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects. RESULTS: The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant. CONCLUSIONS: The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02425332.

Error Fields: Personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We have shown that augmenting error can enhance learning, but while such findings are encouraging the methods need to be refined to accommodate a person's individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. We also found a relationship between the amount of learning and how much variability was induced by the error augmentation treatments, most likely leading to better exploration and discovery of the causes of error. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better.

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children.

Movement therapy in lung transplantation candidates assisted by a lightweight wearable robot.

The aim of this pilot-study was to investigate the safety, feasibility and tolerability of an assisted mobilization of patients with advanced pulmonary diseases, using a lightweight, exoskeleton-type robot (Myosuit, MyoSwiss AG, Zurich, Switzerland). Ten patients performed activities of daily life (ADL) both with and without the device. The mean age was 53.6 (±5.6) years; 70% were male. The assessment of outcome included the evaluation of vital signs, adverse events, rates of perceived exertion and dyspnea (PRE, PRD), the ability to perform ADL and the individual acceptability. Robotic-assisted mobilization was feasible in all patients. No adverse events occurred. RPE and RPD showed no significant difference with or without the Myosuit (mean difference in RPE -1.7, 95%-confidence interval (CI) -1.16, 4.49; p = 0.211; mean difference in RPD 0.00, 95%-CI -1.88, 1.88; p = 0.475). 80% of patients were interested to participate in a robotic-assisted training on a regular basis. A robotic exoskeleton-assisted mobilization is safe, feasible, well-tolerated and well-accepted. The results are highly encouraging to further pursue this highly innovative approach.

Usability of an exosuit in domestic and community environments.

BACKGROUND: Exosuits have been shown to reduce metabolic cost of walking and to increase gait performance when used in clinical environment. Currently, these devices are transitioning to private use to facilitate independent training at home and in the community. However, their acceptance in unsupervised settings remains unclear. Therefore, the aim of this study was to investigate end-user perspectives and the adoption of an exosuit in domestic and community settings. METHODS: We conducted a mixed-method study to investigate the usability and user experience of an exosuit, the Myosuit. We leveraged on a cohort of seven expert users, who had the device available at home for at least 28 days. Each participant completed two standardized questionnaires (SUS and QUEST) and one personalized, custom questionnaire. Furthermore, a semi-structured interview with each participant was recorded, verbatim transcribed and analyzed using descriptive thematic analysis. Data collected from device sensors quantified the frequency of use. RESULTS: A mean SUS score of 75.4 out of 100 was reported. Five participants scored above the threshold for above-average usability. Participants also expressed high satisfaction with most of the technical features in the QUEST with an average score of 4.1 (3.86-4.71) out of 5. Participants used the Myosuit mainly for walking outside and exercising at home. However, the frequency of use did not meet the recommendations for physical activity established by the World Health Organization. Five participants used the Myosuit approximately once per week. The two other participants integrated the device in their daily life and used the Myosuit to a greater extent (approx. five times per week). Major factors that prevented an extensive use of the technology were: (i) difficulties in donning that led to (ii) lack of independence and (iii) lack of motivation in exercising. CONCLUSIONS: Although usable for various activities and well perceived, the adoption of the exosuit in domestic and community settings is yet limited. Use outside the clinic poses further challenges that should be considered when developing new wearable robots. Primarily, design should meet the users' claim for independence and increased adjustability of the device.

A stretchable sensor for force estimation in soft wearable robots.

Soft wearable robots to assist human movements, such as exosuits, have rapidly gained attention thanks to their compliance, low weight and accessibility. However, force measurement in exosuits still rely on load cells and rigid sensors that are not wearable or unsuitable for applications outside the lab. Soft, stretchable and lightweight sensors that become invisible when integrated in an exosuit and perfectly conform to the human body represent a promising alternative. In this work, we developed a wearable sensing system based on a soft stretchable silicone-based strain gauge to measure the forces acting in the passive elastic elements of an exosuit. To measure sensor's accuracy, two unimpaired participants walked on a treadmill at speeds between 0.9 and 2.1 $\text{m}\text{s}^{-1}$. When comparing our solution to a state-of-the-art motion capture system, we found an average root mean square error in force estimation of 12.5% and a standard deviation of 7.4%. Furthermore, we showed the portability of our sensory system by monitoring the forces exerted by the wearable robot during outdoor walking. Our study shows the potential of using stretchable sensors to monitor walking patterns in studies outside the lab and to control human-robot interaction.