A review of gait disorders in the elderly and neurological patients for robot-assisted training

Purpose: Ambulation is an important objective for people with pathological gaits. Exoskeleton robots can assist these people to complete their activities of daily living. There are exoskeletons that have been presented in literature to assist the elderly and other pathological gait users. This article presents a review of the degree of support required in the elderly and neurological gait disorders found in the human population. This will help to advance the design of robot-assisted devices based on the needs of the end users. Methods: The articles included in this review are collected from different databases including Science Direct, Springer Link, Web of Science, Medline and PubMed and with the purpose to investigate the gait parameters of elderly and neurological patients. Studies were included after considering the full texts and only those which focus on spatiotemporal, kinematic and kinetic gait parameters were selected as they are most relevant to the scope of this review. A systematic review and meta-analysis were conducted. Results: The meta-analysis report on the spatiotemporal, kinematic and kinetic gait parameters of elderly and neurological patients revealed a significant difference based on the type and level of impairment. Healthy elderly population showed deviations in the gait parameters due to age, however, significant difference is observed in the gait parameters of the neurological patients. Conclusion: A level of agreement was observed in most of the studies however the review also noticed some controversies among different studies in the same group. The review on the spatiotemporal, kinematics and kinetic gait parameters will provide a summary of the fundamental needs of the users for the future design and development of robotic assistive devices. Implications for rehabilitation The support requirements provide the foundation for designing assistive devices. The findings will be crucial in defining the design criteria for robot assistive devices

Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Motor Electrical Damping for Back-Drivable Prosthetic Knee

The paper presents a model and analysis of a backdrivable knee prosthesis. In this context, the investigation into the design, modelling and analysis of a back-drivable semiactive prosthetic knee is presented. A mathematical model has been developed for evaluating the electrical damping characteristics of the DC motor in passive mode. The analysis shows that a single actuator could be suitable to work in active mode to provide mechanical power and in passive mode as a damper dissipating energy

A Review of Assistive Robotic Exoskeletons and Mobility Disorders in Children to Establish Requirements of Such Devices for Paediatric Population

There has been growing interest in robotic exoskeletons over the past two decades, and the use of robotic exoskeletons has increased with the development of technology and wider awareness of their benefits. Although there have been numerous studies in the area of robotic exoskeletons, the research appears to have neglected paediatric population end users. Possible reasons behind this could be the continuous growth of children which affects the requirements of the system and also relatively fewer number of immobilized subjects in the paediatric population compared to adult population. In this paper, firstly a review of state of the art of assistive robotic exoskeletons highlighting the lack of research for paediatric population is presented. Secondly, different mobility disorders in children and system requirements of an assistive robotic exoskeleton for these disorders are addressed

Portable haptic device for lower limb amputee gait feedback: Assessing static and dynamic perceptibility

Loss of joints and severed sensory pathway cause reduced mobility capabilities in lower limb amputees. Although prosthetic devices attempt to restore normal mobility functions, lack of awareness and control of limb placement increase the risk of falling and causing amputee to have high level of visual dependency. Haptic feedback can serve as a cue for gait events during ambulation thus providing sense of awareness of the limb position. This paper presents a wireless wearable skin stretch haptic device to be fitted around the thigh region. The movement profile of the device was characterized and a preliminary work with able-bodied participants and an above-knee amputee to assess the ability of users to perceive the delivered stimuli during static and dynamic mode is reported. Perceptibility was found to be increasing with stretch magnitude. It was observed that a higher magnitude of stretch was needed for the stimuli to be accurately perceived during walking in comparison to static standing, most likely due to the intense movement of the muscle and increased motor skills demand during walking activity

Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors

In this paper, a novel approach for recognition of walking activities and gait events with wearable sensors is presented. This approach, called adaptive Bayesian inference system (BasIS), uses a probabilistic formulation with a sequential analysis method, for recognition of walking activities performed by participants. Recognition of gait events, needed to identify the state of the human body during the walking activity, is also provided by the proposed method. In addition, the BasIS system includes an adaptive action-perception method for the prediction of gait events. The adaptive approach uses the knowledge gained from decisions made over time by the inference system. The actionperception method allows the BasIS system to autonomously adapt its performance, based on the evaluation of its own predictions and decisions made over time. The proposed approach is implemented in a layered architecture and validated with the recognition of three walking activities; level-ground, ramp ascent and ramp descent. The validation process employs real data from three inertial measurements units attached to the thigh, shanks and foot of participants while performing walking activities. The experiments show that mean decision times of 240 ms and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. The validation experiments also show that the performance, in accuracy and speed, is not significantly affected when noise is added to sensor measurements. These results show that the proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time. Overall, the adaptive BasIS system demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

Muscle Synergy Analysis in Transtibial Amputee during Ramp Ascending Activity

In developed countries, the highest number of amputees are elderly with transtibial amputation. Walking on inclined surfaces is difficult for amputees due to loss of muscle volume and strength thereby transtibial amputees (TA) rely on the intact limb to maintain stability. The aim of this study was to use the concatenated non-negative matrix factorization (CNMF) technique to calculate muscle synergy components and compare the difference in muscle synergies and their associated activation profiles in the healthy and amputee groups during ramp ascending (RA) activity. Healthy subjects' dominant leg and amputee's intact leg (IL) were considered for recording surface electromyography (sEMG). The muscle synergies comparison showed a reasonable correlation between the healthy and amputee groups. This suggests the central nervous system (CNS) activates the same group of muscles synergistically. However, the activation coefficient profile (C) results indicated statistically significant difference (p <; 0.05) in some parts of the gait cycle (GC) in healthy and amputee groups. The difference exhibited in activation profiles of amputee's IL could be due to the instability of the prosthetic leg during the GC which resulted in alteration of the IL muscles activations. This information will be useful in rehabilitation and in the future development of prosthetic devices by using the IL muscles information to control the prostheses

Real-time gait event detection using a miniature gyroscope to improve rehabilitation for artificial lower limb users

This paper presents a simple heuristic rule based on real-time gait event detection algorithm for transfemoral amputees based on gyroscope signal and validate with footswitches for ramp activities with different inclinatio