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 identification of sit-to-stand and stand-to-sit with a wearable sensor

Identification of human movements is crucial for the design of intelligent devices capable to provide assistance. In this work, a Bayesian formulation, together with a sequential analysis method, is presented for identification of sit-to-stand (SiSt) and stand-to-sit (StSi) activities. This method performs autonomous iterative accumulation of sensor measurements and decision-making processes, while dealing with noise and uncertainty present in sensors. First, the Bayesian formulation is able to identify sit, transition and stand activity states. Second, the transition state, divided into transition phases, is used to identify the state of the human body during SiSt and StSi. These processes employ acceleration signals from an inertial measurement unit attached to the thigh of participants. Validation of our method with experiments in offline, real-time and a simulated environment, shows its capability to identify the human body during SiSt and StSi with an accuracy of 100% and mean response time of 50 ms (5 sensor measurements). In the simulated environment, our approach shows its potential to interact with low-level methods required for robot control. Overall, this work offers a robust framework for intelligent and autonomous systems, capable to recognise the human intent to rise from and sit on a chair, which is essential to provide accurate and fast assistance

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

Learning architecture for the recognition of walking and prediction of gait period using wearable sensors

This work presents a novel learning architecture for the recognition and prediction of walking activity and gait period, respectively, using wearable sensors. This approach is composed of a Convolutional Neural Network (CNN), a Predicted Information Gain (PIG) module and an adaptive combination of information sources. The CNN provides the recognition of walking and gait periods. This information is used by the proposed PIG method to estimate the next most probable gait period along the gait cycle. The outputs from the CNN and PIG modules are combined by a proposed adaptive process, which relies on data from the source that shows to be more reliable. This adaptive combination ensures that the learning architecture provides accurate recognition and prediction of walking activity and gait periods over time. The learning architecture uses data from an array of three inertial measurement units attached to the lower limbs of individuals. The validation of this work is performed by the recognition of level-ground walking, ramp ascent and ramp descent, and the prediction of gait periods. The recognition of walking activity and gait period is 100% and 98.63%, respectively, when the CNN model is employed alone. The recognition of gait periods achieves a 99.9% accuracy, when the PIG method and adaptive combination are also used. These results demonstrate the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, the learning architecture offers an alternative approach for accurate activity recognition, which is essential for the development of wearable robots capable of reliably and safely assisting humans in activities of daily living

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

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

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

Estimation of Actuation System Parameters for Prosthetic Ankle

The loss in mobility following amputation results in a degradation of the quality of life of the amputees as it affects many aspects of their personal and professional lives. Lower limb prostheses are used to replace the lost limbs and assist amputees in restoring their missing mobility functions. Despite the current technological advances in prosthetics, amputees still suffer from gait asymmetry and high metabolic energy costs. The gait asymmetry pattern and high metabolic energy costs occur due to: the inability to deliver the required level of assistance/power at the right time and the inertia and mass distribution asymmetry between the intact and the prosthetic leg

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