Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities

Neuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns of neuromuscular patients due to their non-invasive and cost-efficient characteristics. FSR and IMU sensors are the most popular and efficient options. When assessing abnormal gait patterns, it is important to determine the optimal locations of FSRs and IMUs on the human body, along with their computational framework. The gait abnormalities of different types and the gait analysis systems based on IMUs and FSRs have therefore been investigated. After studying a variety of research articles, the optimal locations of the FSR and IMU sensors were determined by analysing the main pressure points under the feet and prime anatomical locations on the human body. A total of seven locations (the big toe, heel, first, third, and fifth metatarsals, as well as two close to the medial arch) can be used to measure gate cycles for normal and flat feet. It has been found that IMU sensors can be placed in four standard anatomical locations (the feet, shank, thigh, and pelvis). A section on computational analysis is included to illustrate how data from the FSR and IMU sensors are processed. Sensor data is typically sampled at 100 Hz, and wireless systems use a range of microcontrollers to capture and transmit the signals. The findings reported in this article are expected to help develop efficient and cost-effective gait analysis systems by using an optimal number of FSRs and IMUs

Wearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes. The device was conceived in modular form and consists of a separate shirt, trousers, glove, and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification

: Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy

Rhythmic Haptic Cueing for Gait Rehabilitation of Hemiparetic Stroke and Brain Injury Survivors

This thesis explores the gait rehabilitation of hemiparetic stroke and brain injury survivors by a process of haptic entrainment to rhythmic cues. Entrainment to auditory metronomes is known to improve gait; this thesis presents the first systematic study of entrainment for gait rehabilitation via the haptic modality. To investigate this approach, a multi-limb metronome capable of delivering a steady, isochronous haptic rhythm to alternating legs was developed, purpose-built for gait rehabilitation, together with appropriate software for monitoring and assessing gait. A formative observational study, carried out at a specialised neurological centre, supplemented by discussions with physiotherapists and neuropsychologists, was used to focus the scope on hemiparetic stroke and brain injury. A second formative study used a technology probe approach to explore the behaviour of hemiparetic participants under haptic cueing using a pre-existing prototype. Qualitative data was collected by observation of, and discussion with, participants and health professionals. In preparation for a quantitative gait study, a formal experiment was carried out to identify a workable range for haptic entrainment. This led to the creation of a procedure to screen out those with cognitive difficulties entraining to a rhythm, regardless of their walking ability. The final study was a quantitative gait study combining temporal and spatial data on haptically cued participants with hemiparetic stroke and brain injury. Gait characteristics were measured before, during and after cueing. All successfully screened participants were able to synchronise their steps to a haptically presented rhythm. For a substantial proportion of participants, an immediate (though not necessarily lasting) improvement of temporal gait characteristics was found during cueing. Some improvements over baseline occurred immediately afterwards, rather than during, haptic cueing. Design issues and trade-offs are identified, and interactions between perception, sensory deficit, attention, memory, cognitive load and haptic entrainment are noted

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors:A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life

Using wearable inertial sensors to compare different versions of the dual task paradigm during walking

The dual task paradigm (DTP), where performance of a walking task co-occurs with a cognitive task to assess performance decrement, has been controversially mooted as a more suitable task to test safety from falls in outdoor and urban environments than simple walking in a hospital corridor. There are a variety of different cognitive tasks that have been used in the DTP, and we wanted to assess the use of a secondary task that requires mental tracking (the alternate letter alphabet task) against a more automatic working memory task (counting backward by ones). In this study we validated the x-io x-IMU wearable inertial sensors, used them to record healthy walking, and then used dynamic time warping to assess the elements of the gait cycle. In the timed 25 foot walk (T25FW) the alternate letter alphabet task lengthened the stride time significantly compared to ordinary walking, while counting backward did not. We conclude that adding a mental tracking task in a DTP will elicit performance decrement in healthy volunteers