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Abstract There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.

This study presents a novel framework that utilizes instrumented footwear to predict fall risk in institutionalized older adults by leveraging stride-to-stride gait data. The older adults are categorized into fallers and non-fallers using three distinct criteria: retrospective fall history, prospective fall occurrence, and a combination of both retrospective and prospective data. Three types of data collected from N=95 institutionalized older adults are analyzed: traditional timed mobility tests, gait data collected from a validated electronic walkway, and gait data collected with instrumented footwear developed by our team. The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. The models are evaluated using leave-one-out cross-validation and 10-fold cross-validation. The results show that models using gait data from the instrumented footwear outperformed those based on traditional tests and walkway data, with area under the receiver operating characteristic curve (AUC) values for predicting prospective falls being 0.47, 0.66, and 0.80, respectively. The sensitivity of the models increases when they are trained using both past and future falls data, rather than relying solely on past or future falls data. This study demonstrates the potential of instrumented footwear for fall risk assessment in elderly individuals. The findings provide valuable insights for fall prevention and care, highlighting the superior predictive capabilities of the developed system compared to traditional methods.

Extended reality (XR) technology combines physical reality with computer synthetic virtuality to deliver immersive experience to users. Virtual reality (VR) and augmented reality (AR) are two subdomains within XR with different immersion levels. Both of these have the potential to be combined with robot-assisted training protocols to maximize postural control improvement. In this study, we conducted a randomized control experiment with sixty-three healthy subjects to compare the effectiveness of robot-assisted posture training combined with VR or AR against robotic training alone. A robotic Trunk Support Trainer (TruST) was employed to deliver assistive force at the trunk as subjects moved beyond the stability limits during training. Our results showed that both VR and AR significantly enhanced the training outcomes of the TruST intervention. However, the VR group experienced higher simulator sickness compared to the AR group, suggesting that AR is better suited for sitting posture training in conjunction with TruST intervention. Our findings highlight the added value of XR to robot-assisted training and provide novel insights into the differences between AR and VR when integrated into a robotic training protocol. In addition, we developed a custom XR application that suited well for TruST intervention requirements. Our approach can be extended to other studies to develop novel XR-enhanced robotic training platforms.

Goal functions make virtual goal-oriented motor tasks easier to analyze and manipulate by explicitly linking movement to outcome. However, they have only been used to study constrained (e.g., planar) upper limb movements. We present a design framework for integrating goal functions with unconstrained postural and upper limb movements in a virtual reality (VR) device. VR tasks designed with the framework can mimic unconstrained natural motions and thus train a range of functional movements yet remain analytically tractable. We created three in-place VR motor tasks: a bow-and-arrow, a reach-and-strike, and a punching bag task. Each task was adjusted to subject-specific workspace limits and anthropometrics. We studied the effects of 3 days of practice and 3 reach/lean distances on task performance in 12 healthy adults. Subjects performed all tasks on day 1 with moderate proficiency and improved with practice at all reach/lean distances. Task-specific results showed that performance decreased and movement variability increased near the edge of the reaching workspace; viewing angles and the imperfect depth cues in VR likely led to biases in performance and practice could attenuate the former effect; in reach-and-strike, subjects learned movement patterns similar to those seen in a real-world striking sport. These results show that our framework can deliver tasks useful for analyzing and training motor performance and can guide future in-place motor training. Post-hoc, we demonstrated the feasibility of generalizable methods that adjust required movement speeds and task difficulty for impaired populations.

Abstract Late-life balance disorders remain a severe problem with fatal consequences. Perturbation-based balance training (PBT), a form of rehabilitation that intentionally introduces small, unpredictable disruptions to an individual's gait cycle, can improve balance. The Tethered Pelvic Assist Device (TPAD) is a cable-driven robotic trainer that applies perturbations to the user's pelvis during treadmill walking. Earlier work showcased improved gait stability and the first evidence of increased cognition acutely. The mobile Tethered Pelvic Assist Device (mTPAD), a portable version of the TPAD, applies perturbations to a pelvic belt via a posterior walker during overground gait, as opposed to treadmill walking. Forty healthy older adults were randomly assigned to a control group (CG, n = 20) without mTPAD PBT or an experimental group (EG, n = 20) with mTPAD PBT for a two-day study. Day 1 consisted of baseline anthropometrics, vitals, and functional and cognitive measurements. Day 2 consisted of training with the mTPAD and post-interventional cognitive and functional measurements. Results revealed that the EG significantly outperformed the CG in several cognitive (SDMT-C and TMT-B) and functional (BBS and 4-Stage Balance: one-foot stand) measurements while showcasing increased confidence in mobility based on FES-I. To our knowledge, our study is the first randomized, large group (n = 40) clinical study exploring new mobile perturbation-based robotic gait training technology.

Transcutaneous spinal cord stimulation (TSCS) is gaining popularity as a noninvasive alternative to epidural stimulation. However, there is still much to learn about its effects and utility in assisting recovery of motor control. In this study, we applied TSCS to healthy subjects concurrently performing a functional training task to study its effects during a training intervention. We first carried out neurophysiological tests to characterize the H-reflex, H-reflex recovery, and posterior root muscle reflex thresholds, and then conducted balance tests, first without TSCS and then with TSCS. Balance tests included trunk perturbations in forward, backward, left, and right directions, and subjects’ balance was characterized by their response to force perturbations. A balance training task involved the subjects playing a catch-and-throw game in virtual reality (VR) while receiving trunk perturbations and TSCS. Balance tests with and without TSCS were conducted after the VR training to measure subjects’ post-training balance characteristics and then neurophysiological tests were carried out again. Statistical comparisons using t-tests between the balance and neurophysiological data collected before and after the VR training intervention found that the immediate effect of TSCS was to increase muscle activity during forward perturbations and to reduce balance performance in that direction. Muscle activity decreased after training and even more once TSCS was turned off. We thus observed an interaction of effects where TSCS increased muscle activity while the physical training decreased it.

The boundary-based assist-as-needed (BAAN) force field is widely used in robotic rehabilitation and has shown promising results in improving trunk control and postural stability. However, the fundamental understanding of how the BAAN force field affects the neuromuscular control remains unclear. In this study, we investigate how the BAAN force field impacts muscle synergy in the lower limbs during standing posture training. We integrated virtual reality (VR) into a cable-driven Robotic Upright Stand Trainer (RobUST) to define a complex standing task that requires both reactive and voluntary dynamic postural control. Ten healthy subjects were randomly assigned to two groups. Each subject performed 100 trials of the standing task with or without assistance from the BAAN force field provided by RobUST. The BAAN force field significantly improved balance control and motor task performance. Our results also indicate that the BAAN force field reduced the total number of lower limb muscle synergies while concurrently increasing the synergy density (i.e., number of muscles recruited in each synergy) during both reactive and voluntary dynamic posture training. This pilot study provides fundamental insights into understanding the neuromuscular basis of the BAAN robotic rehabilitation strategy and its potential for clinical applications. In addition, we expanded the repertoire of training with RobUST that integrates both perturbation training and goal-oriented functional motor training within a single task. This approach can be extended to other rehabilitation robots and training approaches with them.

Seated postural limit defines the boundary of a region such that for any excursions made outside this boundary a subject cannot return the trunk to the neutral position without additional external support. The seated postural limits can be used as a reference to provide assistive support to the torso by the Trunk Support Trainer (TruST). However, fixed boundary representations of seated postural limits are inadequate to capture dynamically changing seated postural limits during training. In this study, we propose a conceptual model of dynamic boundary of the trunk center by assigning a vector that tracks the postural-goal direction and trunk movement amplitude during a sitting task. We experimented with 20 healthy subjects. The results support our hypothesis that TruST intervention with an assist-as-needed force controller based on dynamic boundary representation could achieve more significant sitting postural control improvements than a fixed boundary representation. The second contribution of this paper is that we provide an effective approach to embed deep learning into TruST’s real-time controller design. We have compiled a 3D trunk movement dataset which is currently the largest in the literature. We designed a loss function capable of solving the gate-controlled regression problem. We have proposed a novel deep-learning roadmap for the exploration study. Following the roadmap, we developed a deep learning architecture, modified the widely used Inception module, and then obtained a deep learning model capable of accurately predicting the dynamic boundary in real-time. We believe that this approach can be extended to other rehabilitation robots towards designing intelligent dynamic boundary-based assist-as-needed controllers.

Robotic gait training may improve overground ambulation for individuals with poor control over pelvic motion. However, there is a need for an overground gait training robotic device that allows full control of pelvic movement and synchronizes applied forces to the user’s gait. This work evaluates an overground robotic gait trainer that applies synchronized forces on the user’s pelvis, the mobile Tethered Pelvic Assist Device. To illustrate one possible control scheme, we apply assistive frontal plane pelvic moments synchronized with the user’s continuous gait in real-time. Ten healthy adults walked with the robotic device, with and without frontal plane moments. The frontal plane moments corresponded to 10% of the user’s body weight with a moment arm of half their pelvic width. The frontal plane moments significantly increased the range of frontal plane pelvic angles from 2.6° to 9.9° and the sagittal and transverse planes from 4.6° to 10.1° and 3.0° to 8.3°, respectively. The frontal plane moments also significantly increased the activation of the left gluteus medius muscle, which assists in regulating pelvic obliquity. The right gluteus medius muscle activation did not significantly differ when frontal plane moments were applied. This work highlights the ability of the mobile Tethered Pelvic Assist Device to apply a continuous pelvic moment that is synchronized with the user’s gait cycle. This capability could change how overground robotic gait training strategies are designed and applied. The potential for gait training interventions that target gait deficits or muscle weakness can now be explored with the mobile Tethered Pelvic Assist Device.

Instrumented footwear represents a promising technology for spatiotemporal gait analysis in out-of-the-lab conditions. However, moderate accuracy impacts this technology’s ability to capture subtle, but clinically meaningful, changes in gait patterns that may indicate adverse outcomes or underlying neurological conditions. This limitation hampers the use of instrumented footwear to aid functional assessments and clinical decision making. This paper introduces new transductive-learning inference models that substantially reduce measurement errors relative to conventional data processing techniques, without requiring subject-specific labelled data. The proposed models use subject-optimized input features and hyperparameters to adjust the spatiotemporal gait metrics (i.e., stride time, length, and velocity, swing time, and double support time) obtained with conventional techniques, resulting in computationally simpler models compared to end-to-end machine learning approaches. Model validity and reliability were evaluated against a gold-standard electronic walkway during a clinical gait performance test (6-minute walk test) administered to N = 95 senior residents of assisted living facilities with diverse levels of gait and balance impairments. Average reductions in absolute errors relative to conventional techniques were −42.0% and −33.5% for spatial and gait-phase parameters, respectively, indicating the potential of transductive learning models for improving the accuracy of instrumented footwear for ambulatory gait analysis.

Abstract Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster’s symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

Stroke is a leading cause of disability, impairing the ability to generate propulsive forces and causing significant lateral gait asymmetry. We aim to improve stroke survivors’ gaits by promoting weight-bearing during affected limb stance. External forces can encourage this; e.g., vertical forces can augment the gravitational force requiring higher ground reaction forces, or lateral forces can shift the center of mass over the stance foot, altering the lateral placement of the center of pressure. With our novel design of a mobile Tethered Pelvic Assist Device (mTPAD) paired with the DeepSole system to predict the user’s gait cycle percentage, we demonstrate how to apply three-dimensional forces on the pelvis without lower limb constraints. This work is the first result in the literature that shows that with an applied lateral force during affected limb stance, the center of pressure trajectory’s lateral symmetry is significantly closer to a 0% symmetry (5.5%) than without external force applied (−9.8%,p<0.05). Furthermore, the affected limb’s maximum relative pressure (p) significantly increases from 233.7p to 234.1p (p<0.05) with an applied downward force, increasing affected limb loading. This work highlights how the mTPAD increases weight-bearing and propulsive forces during gait, which is a crucial goal for stroke survivors.

While walkers are used as mobility aids for different gait impairments, little is known about the factors that affect the performance of such aids. Therefore, we investigated the impact of arm-holding conditions on gait stability and muscle activation. We used surface electromyography (sEMG) sensors on specific arm and leg muscles while the users took laps with a robotic walker, the mobile Tethered Pelvic Assist Device (mTPAD), on an instrumented mat. Eleven participants without gait disorders walked with and without a 10% body weight (BW) force applied on the pelvis in the following three configurations: (i) while gripping the walker’s frame, (ii) while using an armrest with their arms at a 90∘ angle, and (iii) while using an armrest with their arms at a 130∘ angle for 5 min each. Our results showed that when applying a force, the users changed their gait to increase stability. We also discovered differences in muscle activation based on the user’s specific arm conditions. Specifically, the 130∘ condition required the least muscle activation, while gripping the walker’s frame increased specific muscle activation compared to 90∘ and 130∘. This study is the first to evaluate how arm-holding and external loading conditions alter gait and muscle activations using the mTPAD.

Although Cable-driven rehabilitation devices (CDRDs) have several advantages over traditional link-driven devices, including their light weight, ease of reconfiguration, and remote actuation, the majority of existing lower-limb CDRDs are limited to rehabilitation in the sagittal plane. In this work, we proposed a novel three degrees of freedom (DOF) lower limb model which accommodates hip abduction/adduction motion in the frontal plane, as well as knee and hip flexion/extension in the sagittal plane. The proposed model was employed to investigate the feasibility of using bi-planar cable routing to track a bi-planar reference healthy trajectory. Various possible routings of four cable configurations were selected and studied with the 3DOF model. The optimal locations of the hip cuffs were determined using optimization. When compared with the five-cable routing configuration, the four-cable routing produced higher joint forces, which motivated the future study of other potential cable routing configurations and their ability to track bi-planar motion.

Hand pose estimation (HPE) plays an important role during the functional assessment of the hand and in potential rehabilitation. It is a challenge to predict the pose of the hand conveniently and accurately during functional tasks, and this limits the application of HPE. In this paper, we propose a novel architecture of a shifted attention regression network (SARN) to perform HPE. Given a depth image, SARN first predicts the spatial relationships between points in the depth image and a group of hand keypoints that determine the pose of the hand. Then, SARN uses these spatial relationships to infer the 3D position of each hand keypoint. To verify the effectiveness of the proposed method, we conducted experiments on three open-source datasets of 3D hand poses: NYU, ICVL, and MSRA. The proposed method achieved state-of-the-art performance with 7.32 mm, 5.91 mm, and 7.17 mm of mean error at the hand keypoints, i.e., mean Euclidean distance between the predicted and ground-truth hand keypoint positions. Additionally, to test the feasibility of SARN in hand movement recognition, a hand movement dataset of 26K depth images from 17 healthy subjects was constructed based on the finger tapping test, an important component of neurological exams administered to Parkinson’s patients. Each image was annotated with the tips of the index finger and the thumb. For this dataset, the proposed method achieved a mean error of 2.99 mm at the hand keypoints and comparable performance on three task-specific metrics: the distance, velocity, and acceleration of the relative movement of the two fingertips. Results on the open-source datasets demonstrated the effectiveness of the proposed method, and results on our finger tapping dataset validated its potential for applications in functional task characterization.

Lower back pain and musculoskeletal injuries are serious concerns for workers subjected to physical workload and manual material handling tasks. Spine assistive exoskeletons are being developed to support the spine and distribute the spine load. This article presents a detailed up-to-date review on the back support exoskeletons by discussing their type (Active/Passive), structure (Rigid/Soft), power transmission methods, weight, maximum assistive force, battery technologies, tasks (lifting, bending, stooping work), kinematic compatibility and other important features. This article also assesses the back support exoskeletons in terms of their ability to reduce the physical load on the spine. By reviewing functional and structural characteristics, the goal is to increase communication and realization among ergonomics practitioners, developers, customers, and factory workers. The search resulted in reviewing 34 exoskeletons of which 16 were passive and 18 were active. In conclusion, back support exoskeletons have immense potential to significantly reduce the factors regarding work-related musculoskeletal injuries. However, various technical challenges and a lack of established safety standards limit the wide adaptation of exoskeletons in industry.

Abstract Background Damage to the cerebellum can affect neural structures involved in locomotion, causing gait and balance disorders. However, the integrity of cerebellum does not seem to be critical in managing sudden and unexpected environmental changes such as disturbances during walking. The cerebellum also plays a functional role in motor learning. Only a few effective therapies exist for individuals with cerebellar ataxia. With these in mind, we aimed at investigating: (1) corrective response of participants with cerebellar ataxia (CA) to unexpected gait perturbations; and (2) the effectiveness of a perturbation-based training to improve their dynamic stability during balance recovery responses and steady walking. Specifically, we hypothesized that: (1) CA group can show a corrective behavior similar to that of a healthy control group; (2) the exposure to a perturbation-based treatment can exploit residual learning capability, thus improving their dynamic stability during balance recovery responses and steady locomotion. Methods Ten participants with cerebellar ataxia and eight age-matched healthy adults were exposed to a single perturbation-based training session. The Active Tethered Pelvic Assist Device applied unexpected waist-pull perturbations while participants walked on a treadmill. Spatio-temporal parameters and dynamic stability were determined during corrective responses and steady locomotion, before and after the training. The ANalysis Of VAriance was the main statistical test used to assess the effects of group (healthy vs CA) and training (baseline vs post) on spatio-temporal parameters of the gait and margin of stability. Results Data analysis revealed that individuals with cerebellar ataxia behaved differently from healthy volunteers: (1) they retained a wider base of support during corrective responses and steady gait both before and after the training; (2) due to the training, patients improved their anterior-posterior margin of stability during steady walking only. Conclusions Our results revealed that participants with cerebellar ataxia could still rely on their learning capability to modify the gait towards a safer behavior. However, they could not take advantage from their residual learning capability while managing sudden and unexpected perturbations. Accordingly, the proposed training paradigm can be considered as a promising approach to improve balance control during steady walking in these individuals.

Aging is accompanied by an alteration in the capacity to ambulate, react to external balance perturbations, and resolve cognitive tasks. Perturbation-based balance training has been used to induce adaptations of gait stability and reduce fall risk. The compensatory reactions generated in response to external perturbations depend on the activation of specific neural structures. This suggests that training balance recovery reactions should show acute cognitive training effects. This study aims to investigate whether exposure to repeated balance perturbations while walking can produce acute aftereffects that improve proactive and reactive strategies to control gait stability and cognitive performance in healthy older adults. It is expected that an adaptation of the recovery reactions would be associated with increased selective attention and information processing speed. Twenty-eight healthy older adults were assigned to either an Experimental (EG) or a Control Group (CG). The protocol was divided in 2 days. During the first visit, all participants completed the Symbol Digit Modalities Test (SDMT) and the Trail Making Test (TMT). During the second visit, a cable-driven robot was used to apply waist-pull perturbations while walking on a treadmill. The EG was trained with multidirectional perturbations of increasing intensity. The CG walked for a comparable amount of time with cables on, but without experiencing perturbations. Before and after the training, all participants were exposed to diagonal waist-pull perturbations. Changes in gait stability were evaluated by comparing the distance between the heel of the leading leg and the extrapolated Center of Mass (Heel-XCoM Distance—HXD) at perturbation onset (PON) and first compensatory heel strike (CHS). Finally, the cables were removed, and participants completed the SDMT and the TMT again. Results showed that only the EG adapted the gait stability (p < 0.001) in reaction to diagonal perturbations and showed improved performance in the SDMT (p < 0.001). This study provides the first evidence that a single session of perturbation-based balance training produce acute aftereffects in terms of increased cognitive performance and gait stability in healthy older adults. Future studies will include measures of functional activation of the cerebral cortex and examine whether a multi-session training will demonstrate chronic effects.

Abstract Objective This article introduces a dynamic neck brace to measure the full range of motion (RoM) of the head–neck. This easy-to-wear brace was used, along with surface electromyography (EMG), to study changes in movement characteristics after neck dissection (ND) in a clinical setting. Methods The brace was inspired by the head–neck anatomy and was designed based on the head–neck movement of 10 healthy individuals. A 6 degrees-of-freedom open-chain structure was adopted to allow full RoM of the head–neck with respect to the shoulders. The physical model was realized by 3D printed materials and inexpensive sensors. Five subjects, who underwent unilateral selective ND, were assessed preoperative and postoperative using this prototype during the head–neck motions. Concurrent EMG measurements of their sternocleidomastoid, splenius capitis, and trapezius muscles were made. Results Reduced RoM during lateral bending on both sides of the neck was observed after surgery, with a mean angle change of 8.03° on the dissected side (95% confidence intervals [CI], 3.11–12.94) and 9.29° on the nondissected side (95% CI, 4.88–13.69), where CI denotes the confidence interval. Axial rotation showed a reduction in the RoM by 5.37° (95% CI, 2.34–8.39) on the nondissection side. Neck extension showed a slight increase in the RoM by 3.15° (95% CI, 0.81–5.49) postoperatively. Conclusions This brace may serve as a simple but useful tool in the clinic to document head–neck RoM changes in patients undergoing ND. Such a characterization may help clinicians evaluate the surgical procedure and guide the recovery of patients.

Scoliosis is a spinal disorder that is conventionally treated using rigid or soft braces. Computational methods such as finite element-based models are used to investigate the mechanics of the spine and the effect of braces. Most spinal braces are either passive, static, or rigid and do not allow mobility to the spine, resulting in muscle atrophy, skin deterioration and other spine complexities. Lack of control over the amount of force being exerted by braces on the human spine could have adverse effects. Therefore, developing an active soft brace which allows mobility to the spine while applying controlled corrective forces could be a promising solution. This study presents finite element analysis (FEA) of an active soft brace that applies corrective forces using elastic bands. The pressure exerted by the brace on the spine can be controlled by varying the tensions in the elastic bands. The elastic band tensions are controlled using low-power, lightweight, and twisted string actuators (TSAs). This study aims to demonstrate the immediate corrections induced by the soft active brace using a scoliotic spine finite element (FE) model. A FE model of the patient’s trunk was created and validated with in vitro study. The brace model was installed on the simulated trunk to evaluate in-brace correction in both sagittal and coronal planes. The brace was evaluated under various load cases by simulating the actuator action.

Based on the parallel robotic manipulator, this paper proposes a motion control strategy for the novel robotic spine brace for spinal rehabilitation exercises. However, several shortcomings of this parallel robotic manipulator, such as dynamic coupling in joint space, low response frequency in roll and pitch directions, and bad influence of device's gravity, result in bad effects on the performance of the robotic spine brace system. For solving these problems of parallel robotic manipulator, a new motion control structure, modal space dynamics-velocity feed-forward (MSDF) motion control strategy, is designed in this paper. A robotic spine brace system model and an actuator dynamic model are expressed using the Kane method. Stability of the robotic system with the MSDF control method is analyzed. For evaluating the performances of the proposed motion control structure, an experimental parallel robotic manipulator is built. Experimental results reveal that the presented MSDF motion control strategy can eliminate those disadvantages efficiently.

Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis.

Hearing loss is a disabling condition that increases with age and has been linked to difficulties in walking and increased risk of falls. The purpose of this study is to investigate changes in gait parameters associated with hearing loss in a group of older adults aged 60 or greater. Custom-engineered footwear was used to collect spatiotemporal gait data in an outpatient clinical setting. Multivariable linear regression was used to determine the relationship between spatiotemporal gait parameters and high and low frequency hearing thresholds of the poorer hearing ear, the left ear, and the right ear, respectively, adjusting for age, sex, race/ethnicity, and the Dizziness Handicap Inventory–Screening version score. Worsening high and low frequency hearing thresholds were associated with increased variability in double support period. Effects persisted after adjusting for the effects of age and perceived vestibular disability and were greater for increases in hearing thresholds for the right ear compared to the left ear. These findings illustrate the importance of auditory feedback for balance and coordination and may suggest a right ear advantage for the influence of auditory feedback on gait.

Hand exoskeleton potential applications reach further than grasping or assistance during manipulation. In this paper, we present a preliminary study of how this technology can be applied in order to improve performance during standing to help the user to keep balance under perturbations. Non-impaired users wearing a hand exoskeleton gripping a hand rail were pushed by a cable-driven robot, so that their standing equilibrium was perturbed. The center of pressure, surface electromyography, and interaction force data were recorded in order to assess the performance of users and their postural strategy. The results showed that users could keep their balance with the same outcomes using their bare hands and the hand exoskeleton. However, when wearing the exoskeleton, a higher muscular activity was registered in hand flexor muscles. This is also supported by the grasping force, which shows that users stretched their hand more than expected when wearing the hand exoskeleton. This paper concludes that it is possible that the lack of tactile feedback could lead to over compensation in the grasping. Therefore, the next studies will aim to check whether this effect can be reversed by training users to wear the exoskeleton.