Hearing Loss Is Associated with Increased Variability in Double Support Period in the Elderly.

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.

Wearable Biofeedback System to Induce Desired Walking Speed in Overground Gait Training.

Biofeedback systems have been extensively used in walking exercises for gait improvement. Past research has focused on modulating the wearer's cadence, gait variability, or symmetry, but none of the previous works has addressed the problem of inducing a desired walking speed in the wearer. In this paper, we present a new, minimally obtrusive wearable biofeedback system (WBS) that uses closed-loop vibrotactile control to elicit desired changes in the wearer's walking speed, based on the predicted user response to anticipatory and delayed feedback. The performance of the proposed control was compared to conventional open-loop rhythmic vibrotactile stimulation with N = 10 healthy individuals who were asked to complete a set of walking tasks along an oval path. The closed-loop vibrotactile control consistently demonstrated better performance than the open-loop control in inducing desired changes in the wearer's walking speed, both with constant and with time-varying target walking speeds. Neither open-loop nor closed-loop stimuli affected natural gait significantly, when the target walking speed was set to the individual's preferred walking speed. Given the importance of walking speed as a summary indicator of health and physical performance, the closed-loop vibrotactile control can pave the way for new technology-enhanced protocols for gait rehabilitation.

Gait assessment with solesound instrumented footwear in spinal muscular atrophy.

INTRODUCTION: Gait impairment is common in spinal muscular atrophy (SMA) and is described using clinical assessments and instrumented walkways. Continuous over-ground walking has not been studied. METHODS: Nine SMA participants completed the 6-minute walk test (6MWT) and 10-meter walk/run wearing instrumented footwear (SoleSound). Data were simultaneously collected using a reference system (GAITRite). The root-mean-square error (RMSE) indicated criterion validity. The decrease in walking speed represented fatigue. Foot loading patterns were evaluated using force sensors. RESULTS: The RMSE for stride time, length, and velocity ranged from 1.3% to 1.7%. Fatigue was 11.6 ± 9.1%, which corresponded to an average deceleration of 0.37 ± 0.28 mm/s2 . Participants spent most of their stance without heel contact. Forefoot contact occurred early in the gait cycle. CONCLUSIONS: These results suggest that footwear-based devices are an alternative to specialized equipment for gait assessment. Better understanding of gait disturbances should inform ongoing treatment efforts and provide a more sensitive outcome measure. Muscle Nerve 56: 230-236, 2017.

Muscle Synergies of Untrained Subjects during 6 min Maximal Rowing on Slides and Fixed Ergometer.

The slides ergometer (SE) was an improvisation from fixed ergometer (FE) to bridge the gap of mechanics between ergometer rowing and on-water rowing. The specific mechanical constraints of these two types of ergometers may affect the pattern of muscle recruitment, coordination and adaptation. The main purpose of this study was to evaluate the muscle synergy during 6 minutes maximal rowing on slides (SE) and fixed ergometers (FE). The laterality of muscle synergy was also examined. Surface electromyography activity, power output, heart rate, stroke length and stroke rate were analyzed from nine physically active subjects to assess the rowing performance. Physically active subjects, who were not specifically trained in rowing, were chosen to exclude the training effect on muscle synergy. Principal component analysis (PCA) with varimax rotation was applied to extract muscle synergy. Three muscle synergies were sufficient to explain the majority of variance in SE (94.4 ± 2.2 %) and FE (92.8 ± 1.7 %). Subjects covered more rowing distance, exerted greater power output and attained higher maximal heart rate during rowing on SE than on FE. The results proved the flexibility of muscle synergy to adapt to the mechanical constraints. Rowing on SE emphasized on bi-articular muscles contrary to rowing on FE which relied on cumulative effect of trunk and upper limb muscles during propulsive phase. Key pointsThree muscle synergies were extracted during maximal rowing on both fixed and slides ergometerUntrained subjects emphasized leg muscles while rowing on SEUntrained subjects focused on back muscles during FE rowing.

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models.

Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equipment to acquire COP trajectories includes stationary force plates, instrumented treadmills, electronic walkways, and insoles featuring high-density force sensing arrays, all of which are expensive and not widely accessible. This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-of-the-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.59 cm, AP: 1.53 cm) indicated technical validity. In individuals with neuromuscular conditions, COP-derived metrics showed significant correlations with validated clinical measures of ambulatory function and lower-extremity muscle strength, providing proof-of-concept evidence of the convergent validity of the proposed method for clinical applications.

Instrumented insoles for assessment of gait in patients with vestibular schwannoma.

Background: Imbalance and gait disturbances are common in patients with vestibular schwannoma (VS) and can result in significant morbidity. Current methods for quantitative gait analysis are cumbersome and difficult to implement. Here, we use custom-engineered instrumented insoles to evaluate the gait of patients diagnosed with VS. Methods: Twenty patients with VS were recruited from otology, neurosurgery, and radiation oncology clinics at a tertiary referral center. Functional gait assessment (FGA), 2-minute walk test (2MWT), and uneven surface walk test (USWT) were performed. Custom-engineered instrumented insoles, equipped with an 8-cell force sensitive resistor (FSR) and a 9-degree-of-freedom inertial measurement unit (IMU), were used to collect stride-by-stride spatiotemporal gait parameters, from which mean values and coefficients of variation (CV) were determined for each patient. Results: FGA scores were significantly correlated with gait metrics obtained from the 2MWT and USWT, including stride length, stride velocity, normalized stride length, normalized stride velocity, stride length CV, and stride velocity CV. Tumor diameter was negatively associated with stride time and swing time on the 2MWT; no such association existed between tumor diameter and FGA or DHI. Conclusions: Instrumented insoles may unveil associations between VS tumor size and gait dysfunction that cannot be captured by standardized clinical assessments and self-reported questionnaires.

Erratum: Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach - ERRATUM.

[This corrects the article DOI: 10.1017/wtc.2022.23.].

Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach.

An active lifestyle can mitigate physical decline and cognitive impairment in older adults. Regular walking exercises for older individuals result in enhanced balance and reduced risk of falling. In this article, we present a study on gait monitoring for older adults during walking using an integrated system encompassing an assistive robot and wearable sensors. The system fuses data from the robot onboard Red Green Blue plus Depth (RGB-D) sensor with inertial and pressure sensors embedded in shoe insoles, and estimates spatiotemporal gait parameters and dynamic margin of stability in real-time. Data collected with 24 participants at a community center reveal associations between gait parameters, physical performance (evaluated with the Short Physical Performance Battery), and cognitive ability (measured with the Montreal Cognitive Assessment). The results validate the feasibility of using such a portable system in out-of-the-lab conditions and will be helpful for designing future technology-enhanced exercise interventions to improve balance, mobility, and strength and potentially reduce falls in older adults.

Transductive Learning Models for Accurate Ambulatory Gait Analysis in Elderly Residents of Assisted Living Facilities.

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.

Optimal time lags from causal prediction model help stratify and forecast nervous system pathology.

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.

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models.

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 115% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.

Immediate Effects of Force Feedback and Plantar Somatosensory Stimuli on Inter-limb Coordination During Perturbed Walking.

Single-sided motor weakness, also known as hemiparesis, is the most prevalent gait impairment among stroke survivors, which often results in gait asymmetry. Studies on robot-assisted gait training (RAGT) have shown positive effects of force feedback on spatial symmetry; somatosensory stimulation is thought to facilitate recovery of temporal symmetry. Despite the known importance of sensorimotor integration for motor recovery, interventions that incorporate RAGT and somatosensory stimuli have been largely overlooked so far. In this paper, we explore how gait symmetry can be restored in healthy subjects following unilateral foot perturbations, using adaptive assistive forces and plantar vibrotactile stimuli provided by a bilateral powered ankle-foot orthosis. Results suggest that combined force feedback and vibrotactile stimuli may be more effective than force feedback alone in reducing spatial asymmetry. Further, force feedback did not produce significant improvements in temporal symmetry, unlike the combined modality. We discuss possible implications of these preliminary findings for future training paradigms for RAGT.

Improving the Accuracy of Wearable Sensors for Human Locomotion Tracking Using Phase-Locked Regression Models.

The trend toward soft wearable robotic systems creates a compelling need for new and reliable sensor systems that do not require a rigid mounting frame. Despite the growing use of inertial measurement units (IMUs) in motion tracking applications, sensor drift and IMU-to-segment misalignment still represent major problems in applications requiring high accuracy. This paper proposes a novel 2-step calibration method which takes advantage of the periodic nature of human locomotion to improve the accuracy of wearable inertial sensors in measuring lower-limb joint angles. Specifically, the method was applied to the determination of the hip joint angles during walking tasks. The accuracy and precision of the calibration method were accessed in a group of N =8 subjects who walked with a custom-designed inertial motion capture system at 85% and 115% of their comfortable pace, using an optical motion capture system as reference. In light of its low computational complexity and good accuracy, the proposed approach shows promise for embedded applications, including closed-loop control of soft wearable robotic systems.

Variable Damping Force Tunnel for Gait Training Using ALEX III.

Haptic feedback affects not only the quality of training but can also influence the physical design of robotic gait trainers by determining how much force needs to be applied to the user and the nature of the force. This paper presents the design of a variable damping force tunnel and explores the effect of the shape and strength of the damping field using ALEX III, a treadmill-based exoskeleton developed at Columbia University. The study consists of 32 healthy subjects who were trained for 40 minutes in the device. The subjects were trained to follow a footpath with a 50% increase in step height, so the foot would have 1.5 times the ground clearance. Subjects were assigned to one of four groups: linear high, linear low, parabolic high, and parabolic low. Linear or parabolic denotes the shape of the damping field, and high or low denotes the rate of change (strength) of the field based on error. It is shown that the new controller is capable of inducing gait adaptations in healthy individuals while walking in the device. All groups showed adaptations in step height, while only the high strength groups showed changes in normalized error area, a measure of how closely the desired path was followed.

Dizziness Handicap Inventory Score Is Highly Correlated With Markers of Gait Disturbance.

OBJECTIVE: To evaluate the association between Dizziness Handicap Inventory-Screening version (DHI-S) score and spatiotemporal gait parameters using SoleSound, a newly developed, inexpensive, portable footwear-based gait analysis system. STUDY DESIGN: Cross-sectional. PATIENTS: One hundred eighteen patients recruited from otology clinic. INTERVENTION(S): Subjects completed the DHI-S survey and four uninterrupted walking laps wearing SoleSound instrumented footwear on a hard, flat surface for 100 m. MAIN OUTCOME MEASURE(S): For each subject, mean and coefficient of variation (CV) of stride length, cadence, walking speed, foot-ground clearance, double-support time, swing period, and stance-to-swing were computed by considering 40 strides of steady-state walking within each lap. Linear regression models were employed to study correlations between these variables and DHI-S scores after adjusting for age, sex, and race/ethnicity. RESULTS: Patients with higher DHI-S score took shorter steps and less steps per minute (-0.017 m and -1.1 steps/min per every four-point increase in DHI-S score, p < 0.05) than patients with a lower DHI-S score, with slower walking speed (-0.025 m/s per every four-point increase in DHI-S score, p < 0.01). Additionally, patients with higher DHI-S scores showed larger variability in all analyzed temporal parameters (+0.1% for CV of cadence, +0.5% for CV of double support period, +0.2% for CV of swing period, and +0.4% for CV of stance-to-swing, per every four-point increase in DHI-S score, p < 0.01). CONCLUSION: SoleSound was effective in measuring a wide range of gait parameters. Patients' self-perception of vestibular handicap, as assessed with DHI-S, is associated with deterioration in measurable gait parameters independent of age.

Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates With Motor Adaptation in Gait Training.

Lower-extremity robotic exoskeletons are used in gait rehabilitation to achieve functional motor recovery. To date, little is known about how gait training and post-training are characterized in brain signals and their causal connectivity. In this work, we used time-domain partial Granger causality (PGC) analysis to elucidate the directed functional connectivity of electroencephalogram (EEG) signals of healthy adults in robot-assisted gait training (RAGT). Our results confirm the presence of EEG rhythms and corticomuscular relationships during standing and walking using spectral and coherence analyses. The PGC analysis revealed enhanced connectivity close to sensorimotor areas ( C3 and CP3 ) during standing, whereas additional connectivities involve the centroparietal ( CP z) and frontal ( F z ) areas during walking with respect to standing. In addition, significant fronto-centroparietal causal effects were found during both training and post-training. Strong correlations were also found between kinematic errors and fronto-centroparietal connectivity during training and post-training. This study suggests fronto-centroparietal connectivity as a potential neuromarker for motor learning and adaptation in RAGT.

Validation of a Footwear-Based Gait Analysis System With Action-Related Feedback.

Quantitative gait analysis enables clinicians to evaluate patient mobility and to diagnose neuromuscular disorders. The clinical application of gait analysis is currently limited by the high operating costs of gait laboratories. The use of instrumented footwear that performs out of the lab measurements on subjects' walking patterns is a promising way to overcome this limitation. Besides serving as assessment tools, such devices can also act as retraining tools that help regulate a patient's gait with acoustic or vibrotactile stimuli.

Assist-as-Needed Robot-Aided Gait Training Improves Walking Function in Individuals Following Stroke.

A novel robot-aided assist-as-needed gait training paradigm has been developed recently. This paradigm encourages subjects' active participation during training. Previous pilot studies demonstrated that assist-as-needed robot-aided gait training (RAGT) improves treadmill walking performance post-stroke. However, it is not known if there is an over-ground transfer of the training effects from RAGT on treadmill or long-term retention of the effects. The purpose of the current study was to examine the effects of assist-as-needed RAGT on over-ground walking pattern post-stroke. Nine stroke subjects received RAGT with visual feedback of each subject's instantaneous ankle malleolus position relative to a target template for 15 40-minute sessions. Clinical evaluations and gait analyses were performed before, immediately after, and 6 months post-training. Stroke subjects demonstrated significant improvements and some long-term retention of the improvements in their self-selected over-ground walking speed, Dynamic Gait Index, Timed Up and Go, peak knee flexion angle during swing phase and total hip joint excursion over the whole gait cycle for their affected leg . These preliminary results demonstrate that subjects improved their over-ground walking pattern and some clinical gait measures post-training suggesting that assist-as-needed RAGT including visual feedback may be an effective approach to improve over-ground walking pattern post-stroke.

The value of robotic systems in stroke rehabilitation.

In this paper, we discuss robot-mediated neurorehabilitation as a significant emerging field in clinical medicine. Stroke rehabilitation is advancing toward more integrated processes, using robotics to facilitate this integration. Rehabilitation approaches have tremendous value in reducing long-term impairments in stroke patients during hospitalization and after discharge, of which robotic systems are a new modality that can provide more effective rehabilitation. The function of robotics in rehabilitative interventions has been examined extensively, generating positive yet not completely satisfactory clinical results. This article presents state-of-the-art robotic systems and their prospective function in poststroke rehabilitation of the upper and lower limbs.

Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.

Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A frontoparietal connection was found in all robot-assisted training sessions. Following training, a causal "top-down" cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.