A Wearable Skin Stretch Haptic Feedback Device: Towards Improving Balance Control in Lower Limb Amputees

Haptic feedback to lower limb amputees is essential to maximize the functionality of a prosthetic device by providing information to the user about the interaction with the environment and the position of the prostheses in space. Severed sensory pathway and the absence of connection between the prosthesis and the Central Nervous System (CNS) after lower limb amputation reduces balance control, increases visual dependency and increases risk of falls among amputees. This work describes the design of a wearable haptic feedback device for lower limb amputees using lateral skin-stretch modality intended to serve as a feedback cue during ambulation. A feedback scheme was proposed based on gait event detection for possible real-time postural adjustment. Preliminary perceptual test with healthy subjects in static condition was carried out and the results indicated over 98% accuracy in determining stimuli location around the upper leg region, suggesting good perceptibility of the delivered stimuli

Real-time gait event detection for transfemoral amputees during ramp ascending and descending

Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and ampute-eprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results

Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction

Falls in older adults are a major clinical problem often resulting in serious injury. The costly nature of clinic-based testing for the propensity of falling and a move towards homebased care and monitoring of older adults has led to research in wearable sensing technologies for identifying fall-related parameters from activities of daily living. This paper discusses the development of two algorithms for identifying periods of walking (gait events) and extracting characteristic patterns for each gait event (gait features) with a view to identifying the propensity to fall in older adults. In this paper, we present an evaluation of the algorithms involving a small real-world dataset collected from healthy adults in an uncontrolled environment. 92.5% of gait events were extracted from lower leg gyroscope data from 5 healthy adults (total duration of 33 hours) and over 95% of the gait characteristic points were identified in this data. A user interface to aid clinicians review gait features from walking events captured over multiple days is also proposed. The work presents initial steps in the development of a platform for monitoring patients within their daily routine in uncontrolled environments to inform clinical decision-making related to falls

Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running.

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro-Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84-100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis

Free-living and laboratory gait characteristics in patients with multiple sclerosis.

BACKGROUND: Wearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated. METHODS: Fourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5-6.0 and EDSS 5.5-5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5-50 steps), intermediate (iWB, 51-100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA. RESULTS: W-CWT compared to S-REF showed good gait event accuracy (0.05-0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%-58.2%) and continuous walking (11.2%-76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased. CONCLUSIONS: This study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking

Gait event detection in controlled and real-life situations: repeated measures from healthy subjects

A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This paper fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finite-state machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from 11 healthy subjects (28.27 +/- 4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 to 4.5 km/h) and slopes (from 0% to 10%). This validation also includes heterogeneous gait patterns from nine healthy subjects (27 +/- 7.35 years) monitored at three self-selected paces (from 1 +/- 0.2 to 2 +/- 0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate (p > 0.9925) and time effective ( 0.9314) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy > 96.98%). Misdetection was more pronounced in middle mid swing (accuracy > 90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.This work has been supported in part by the Fundacao para a Ciencia e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, by the Reference Project under Grant UID/EEA/04436/2013, and part by the FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI)-with the Reference Project under Grant POCI-01-0145-FEDER-006941, and in part by Spanish Ministry of Economy and Competitiveness Grant RYC-2014-16613

Design and validation of a multi-task, multi-context protocol for real-world gait simulation

Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN—12246987

Heuristic Real-Time Detection of Temporal Gait Events for Lower Limb Amputees

This paper presents a complete system and algorithm to estimate temporal gait events during stance and inner-stance phases using a single inertial measurement unit (IMU) in real-time. Validation of the proposed system was carried out by placing the foot-switches (FSW) directly underneath the foot. The performance of the system was assessed with eleven control subjects (CS), one unilateral transfemoral amputee (TFA), and one unilateral transtibial amputee (TTA), while performing level ground walk and ramp activities. The experimental results showed reasonable agreement in timing differences of all the gait events in both groups when compared against the reference system. However, high data latency was observed for TFA in the case of Foot-Flat Start (FFS) and Heel-Off (HO). The slight variation in the positioning of IMU on the shank and the foot-switches underneath the foot and the difference in the kinematics of CS and lower limb amputees are probable reasons for large variations in the time difference. Overall, the detection accuracy was found to be 100% for Initial Contact, FFS, and Toe-Off, and 98.3% for HO. In addition, a high correlation was observed between estimated stance phase duration (SPD) from IMU and the SPD from FSW data. The proposed system showed high accuracy in the detection of temporal gait events which could potentially be employed in the gait analysis applications and the finite-state control of lower limb prostheses/orthoses