A Two-Axis Goniometric Sensor for Tracking Finger Motion

The study of finger kinematics has developed into an important research area. Various hand tracking systems are currently available; however, they all have limited functionality. Generally, the most commonly adopted sensors are limited to measurements with one degree of freedom, i.e., flexion/extension of fingers. More advanced measurements including finger abduction, adduction, and circumduction are much more difficult to achieve. To overcome these limitations, we propose a two-axis 3D printed optical sensor with a compact configuration for tracking finger motion. Based on Malus’ law, this sensor detects the angular changes by analyzing the attenuation of light transmitted through polarizing film. The sensor consists of two orthogonal axes each containing two pathways. The two readings from each axis are fused using a weighted average approach, enabling a measurement range up to 180 ∘ and an improvement in sensitivity. The sensor demonstrates high accuracy (±0.3 ∘ ), high repeatability, and low hysteresis error. Attaching the sensor to the index finger’s metacarpophalangeal joint, real-time movements consisting of flexion/extension, abduction/adduction and circumduction have been successfully recorded. The proposed two-axis sensor has demonstrated its capability for measuring finger movements with two degrees of freedom and can be potentially used to monitor other types of body motion

A Basic Study on Temporal Parameter Estimation of Wheelchair Propulsion based on Measurement of Upper Limb Movements Using Inertial Sensors

Wheelchairs are the most widely used assistive device to aid activities of daily living (ADL) for disabled people. However, manual pushing of a wheelchair frequently leads to overuse of upper extremities causing shoulder pain and carpal tunnel syndrome. The purpose of this study was to test a novel method of estimating temporal parameters of wheelchair propulsion using inertial sensors. In this paper, normalized coordinate values of the vector defined on the upper arm were calculated from an inertial sensor attached on the upper arm. The number of strokes and push cycle timings including duration of propulsion and recovery phases were estimated for steady state wheelchair propulsion. This estimation was completed using a novel vector-based approach and a previously published resultant acceleration method; both were compared to timings measured using the SmartWheel. Measurements were performed on level and sloped surfaces with 10 able bodied subjects. The vector-based method improved estimation of the number of strokes when compared to the resultant acceleration method. However, the push cycle was estimated with better accuracy by the resultant acceleration method. Therefore, a combination of the vector-based and the resultant acceleration methods is proposed to ensure more accurate estimation of temporal parameters. The results suggest inertial sensors can be used to measure wheelchair activity accurately and reliably

Can inertial measurement unit sensors evaluate foot kinematics in drop foot patients using functional electrical stimulation?

The accuracy of inertial measurement units (IMUs) in measuring foot motion in the sagittal plane has been previously compared to motion capture systems for healthy and impaired participants. Studies analyzing the accuracy of IMUs in measuring foot motion in the frontal plane are lacking. Drop foot patients use functional electrical stimulation (FES) to improve walking and reduce the risk of tripping and falling by improving foot dorsiflexion and inversion-eversion. Therefore, this study aims to evaluate if IMUs can estimate foot angles in the frontal and sagittal planes to help understand the effects of FES on drop foot patients in clinical settings. Two Gait Up sensors were used to estimate foot dorsi-plantar flexion and inversion-eversion angles in 13 unimpaired participants and 9 participants affected by drop foot while walking 6 m in a straight line. Unimpaired participants were asked to walk normally at three self-selected speeds and to simulate drop foot. Impaired participants walked with and without FES assistance. Foot angles estimated by the IMUs were compared with those measured from a motion capture system using curve RMSE and Bland Altman limits of agreement. Between participant groups, overall errors of 7.95° ± 3.98°, −1.12° ± 4.20°, and 1.38° ± 5.05° were obtained for the dorsi-plantar flexion range of motion, dorsi-plantar flexion at heel strike, and inversion-eversion at heel strike, respectively. The between-system comparison of their ability to detect dorsi-plantar flexion and inversion-eversion differences associated with FES use on drop foot patients provided limits of agreement too large for IMUs to be able to accurately detect the changes in foot kinematics following FES intervention. To the best of the authors' knowledge, this is the first study to evaluate IMU accuracy in the estimation of foot inversion-eversion and analyze the potential of using IMUs in clinical settings to assess gait for drop foot patients and evaluate the effects of FES. From the results, it can be concluded that IMUs do not currently represent an alternative to motion capture to evaluate foot kinematics in drop foot patients using FES

Design of a knee orthosis locking system

Dissertação de mestrado em Engenharia MecatrónicaThe main goal of this work was to design a mechatronic locking system for a Stance Control Knee Ankle Foot Orthosis (SCKAFO). This mechanism should be able to perform two different functions. The first one is to lock the orthosis during the stance phase of human gait, in which contact between the foot and the ground exists. The second function deals with the unlock of the orthosis during the swing phase, in which there is no contact between the foot and the ground, allowing the flexion of the knee. Biomechanics of human gait play an important role in the mechanical design of the locking system, since the motion characteristics associated with pathological and nonpathological exhibit different behaviors. Thus experimental gait studies was considered for pathological and non-pathological, in order to analyze the kinematic properties(joint angles and trajectories) and kinetic (ground reaction forces, joint forces and moments) of the human gait. In the context of the present work sensors were used to detect the key points that characterize the human gait, allowing for the correct mechanism performance. These sensors are placed in anatomical relevant locations and calculate, not only the joint angles, but also the angular acceleration. The data read by these sensors is interpreted by a microcontroller that controls the actuation system in order to lock or unlock the mechanism. An innovative solution is presented here, which differs from the currently available solutions or in the scientific literature. The new approach is able to work without foot sensors and cables used with the purpose to lock/unlock the orthosis. With this approach it is expected that the locking/unlocking operation will be effective, safe and quick for the user.O objetivo principal deste projeto foi desenvolver um sistema mecatrónico para ortóteses do tipo Stance Control Knee Ankle Foot Orthosis (SCKAFO). Este mecanismo permite realizar duas funções distintas. A primeira consiste no bloqueio da ortótese durante a fase de apoio da marcha humana, onde se verifica contacto entre o pé e o solo. A segunda função incide no desbloqueio da ortótese durante a fase de balanço da marcha humana, onde não se verifica contacto entre o pé e o solo, permitindo a flexão do joelho. Os conceitos biomecânicos da marcha humana assumem uma elevada importância no projeto mecânico deste mecanismo, uma vez que as características associadas à marcha natural e patológica demonstram comportamentos distintos. Por isso serão consideradas análises experimentais, com o objetivo de caracterizar cinematicamente (ângulos e trajetórias das articulações e segmentos anatómicos) e cineticamente (forças de contacto entre o pé e o solo, momentos e forças nas articulações) a marcha humana. No contexto do presente trabalho foram utilizados sensores de forma a detetar pontoschave da marcha humana, permitindo um correto funcionamento do mecanismo. Os sensores serão colocados nos segmentos anatómicos de maior interesse para este estudo e irão possibilitar o cálculo dos ângulos das articulações e as suas acelerações angulares. A informação gerada pelos sensores será interpretada por um microcontrolador, que irá controlar um sistema de atuação, permitindo bloquear ou desbloquear a ortótese. Com este trabalho, pretende-se desenvolver uma abordagem inovadora, que difere de todas as soluções comerciais e apresentadas na literatura científica. Esta solução permite um funcionamento sem a necessidade de recorrer a sensores plantares (colocados no pé) e sem presença de cabos ao longo do membro inferior. Com esta abordagem pretende-se desenvolver um mecanismo que realize a operação de bloqueio e desbloqueio de modo eficaz, seguro e rápido para o seu utilizador

A Haptic Feedback System for Lower Limb Amputees Based on Gait Event Detection

Lower limb amputation has significant effects on a person’s quality of life and ability to perform activities of daily living. Prescription of prosthetic device post amputation aims to help restore some degrees of mobility function, however studies have shown evidence of low balance confidence and higher risk of falling among amputee community, especially those suffering from above knee amputation. While advanced prostheses offer better control, they often lack a form of feedback that delivers the awareness of the limb position to the prosthetic user while walking. This research presents the development and evaluation of a wearable skinstretch haptic feedback system intended to deliver cues of two crucial gait events, namely the Initial Contact (IC) and Toe-off (TO) to its wearer. The system comprises a haptic module that applies lateral skin-stretch on the upper leg or the trunk, corresponding to the gait event detection module based on Inertial Measurement Unit (IMU) attached at the shank. The design and development iterations of the haptic module is presented, and characterization of the feedback parameters is discussed. The validation of the gait event detection module is carried out and finally the integration of the haptic feedback system is described. Experimental work with healthy subjects and an amputee indicated good perceptibility of the feedback during static and dynamic (walking) condition, although higher magnitude of stretch was required to perceive the feedback during dynamic condition. User response time during dynamic activity showed that the haptic feedback system is suitable for delivering cues of IC and TO within the duration of the stance phase. In addition, feedback delivered in discernible patterns can be learned and adapted by the subjects. Finally, a case study was carried out with an above-knee amputee to assess the effects of the haptic feedback on spatio-temporal gait parameters and on the vertical ground reaction force during treadmill and overground walking. The research presented in this report introduces a novel design of a haptic feedback device. As such, the outcome includes a well-controlled skin-stretch effect which contributes to the research by investigating skin-stretch feedback for conveying discrete event information rather than conveying direction information as presented in other studies. In addition, it is found that stretch magnitude as small as 3 mm could be perceived in short duration of 150 ms during dynamic condition, making it a suitable alternative to other widely investigated haptic modality such as vibration for ambulatory feedback application. With continuous training, the haptic feedback system could possibly benefit lower limb amputees by creating awareness of the limb placement during ambulation, potentially reducing visual dependency and increasing walking confidence

Objective assessment of movement disabilities using wearable sensors

The research presents a series of comprehensive analyses based on inertial measurements obtained from wearable sensors to quantitatively describe and assess human kinematic performance in certain tasks that are most related to daily life activities. This is not only a direct application of human movement analysis but also very pivotal in assessing the progression of patients undergoing rehabilitation services. Moreover, the detailed analysis will provide clinicians with greater insights to capture movement disorders and unique ataxic features regarding axial abnormalities which are not directly observed by the clinicians

Investigating Grip Range of Motion and Force Exerted by Individuals with and without Hand Arthritis during Functional Tasks and while Swinging a Golf Club

Hand arthritis is the leading cause of disability in individuals over the age of 50; resulting in dysfunction and pain, making activities of daily living and recreational activities such as golf difficult. Few studies have been conducted on the biomechanical response of individuals with hand arthritis when performing functional activities. This research quantified hand grip movements and strength differences seen in individuals with hand arthritis. Using a video-based motion capture system (Dartfish), a grip limitation of 17.2% (maximum flexion), and 12.7% (maximum extension) was discovered. A wireless finger force measurement system (FingerTPS), was used to show that larger diameter, softer firmness golf grips assisted in reducing the grip force in individuals with and without hand arthritis during a golf swing. This research will benefit the sport biomechanics and clinical fields, providing quantitative results to develop more sophisticated joint protection devices and gain a better understanding of hand arthritis mechanics

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions

Classifying gait alterations using an instrumented smart sock and deep learning

This paper presents a non-invasive method of classifying gait patterns associated with various movement disorders and/or neurological conditions, utilising unobtrusive, instrumented socks and a deep learning network. Seamless instrumented socks were fabricated using three accelerometer embedded yarns, positioned at the toe (hallux), above the heel and on the lateral malleolus. Human trials were conducted on 12 able-bodied participants, an instrumented sock was worn on each foot. Participants were asked to complete seven trials consisting of their typical gait and six different gait types that mimicked the typical movement patterns associated with various movement disorders and neurological conditions. Four Neural Networks and an SVM were tested to ascertain the most effective method of automatic data classification. The Bi-LSTM generated the most accurate results and illustrates that the use of three accelerometers per foot increased classification accuracy compared to a single accelerometer per foot by 11.4%. When only a single accelerometer was utilised for classification, the ankle accelerometer generated the most accurate results in comparison to the other two. The network was able to correctly classify five different gait types: stomp (100%), shuffle (66.8%), diplegic (66.6%), hemiplegic (66.6%) and “normal walking” (58.0%). The network was incapable of correctly differentiating foot slap (21.2%) and steppage gait (4.8%). This work demonstrates that instrumented textile socks incorporating three accelerometer yarns were capable of generating sufficient data to allow a neural network to distinguish between specific gait patterns. This may enable clinicians and therapists to remotely classify gait alterations and observe changes in gait during rehabilitation