A practical design and implementation of a low cost platform for remote monitoring of lower limb health of amputees in the developing world

In many areas of the world accessing professional physicians ‘when needed/as needed’ might not be always possible for a variety of reasons. Therefore, in such cases a targeted e-Health solution to safeguard patient long-term health could be a meaningful approach. Today’s modern healthcare technologies, often built around electronic and computer-based equipment, require an access to a reliable electricity supply. Many healthcare technologies and products also presume access to the high speed internet is available, making them unsuitable for use in areas where there is no fixed-line internet connectivity, access is slow, unreliable and expensive, yet where the most benefit to patients may be gained. In this paper a full mobile sensor platform is presented, based around readily-purchased consumer components, to facilitate a low cost and efficient means of monitoring the health of patients with prosthetic lower limbs. This platform is designed such that it can also be operated in a standalone mode i.e. in the absence of internet connectivity, thereby making it suitable to the developing world. Also, to counter the challenge of power supply issues in e-Health monitoring, a self-contained rechargeable solution to the platform is proposed and demonstrated. The platform works with an Android mobile device, in order to allow for the capture of data from a wireless sensor unit, and to give the clinician access to results from the sensors. The results from the analysis, carried out within the platform’s Raspberry Pi Zero, are demonstrated to be of use for remote monitoring. This is specifically targeted for monitoring the tissue health of lower limb amputees. The monitoring of residual limb temperature and gait can be a useful indicator of tissue viability in lower limb amputees especially those suffering from diabetes. We describe a route wherein non-invasive monitoring of tissue health is achievable using the Gaussian process technique. This knowledge will be useful in establishing biomarkers related to a possible deterioration in a patient’s health or for assessing the impact of clinical interventions

Gait rehabilitation monitor

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento numa base de dados local. Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha durante as sessões de monitorização. Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e o conjunto de IMUs através de Bluetooth. A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de classificação para identificar os tipos de marcha normais e patológicos. O sistema fornece uma REST API disponível num servidor backend recorrendo a bibliotecas Java e Python e a uma base de dados PostgreSQL. O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees. Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais recolhidos e processados para treinar o classificador. Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para treinar um modelo e testar o classificador

Validation, optimization and exploitation of orientation measurements issued from inertial systems for clinical biomechanics

Les centrales inertielles (triade de capteurs inertiels dont la fusion des données permet l’estimation de l’orientation d’un corps rigide) sont de plus en plus populaires en biomécanique. Toutefois, les qualités métrologiques des centrales inertielles (CI) sont peu documentées et leur capacité à identifier des incapacités liées à la mobilité, sous-évaluée. Objectifs : (i) Caractériser la validité de la mesure d’orientation issue de CI ; (ii) Optimiser la justesse et la fidélité de ces mesures; et (iii) Proposer des métriques de mobilité basées sur les mesures d’orientation issues de CI. Méthodologie et résultats : La validité de la mesure d’orientation de différents types de CI a d’abord été évaluée en conditions contrôlées, à l’aide d’une table motorisée et d’une mesure étalon. Il a ainsi été démontré que les mesures d’orientation issues de CI ont une justesse acceptable lors de mouvements lents (justesse moyenne ≤ 3.1º), mais que cette justesse se dégrade avec l’augmentation de la vitesse de rotation. Afin d’évaluer l’impact de ces constatations en contexte clinique d’évaluation de la mobilité, 20 participants ont porté un vêtement incorporant 17 CI lors de la réalisation de diverses tâches de mobilité (transferts assis-debout, marche, retournements). La comparaison des mesures des CI avec celles d’un système étalon a permis de dresser un portrait descriptif des variations de justesse selon la tâche exécutée et le segment/l’articulation mesuré. À partir de ces constats, l’optimisation de la mesure d’orientation issue de CI est abordée d’un point de vue utilisateur, démontrant le potentiel d’un réseau de neurones artificiel comme outil de rétroaction autonome de la qualité de la mesure d’orientation (sensibilité et spécificité ≥ 83%). Afin d’améliorer la robustesse des mesures de cinématique articulaire aux variations environnementales, l’ajout d’une photo et d’un algorithme d’estimation de pose tridimensionnelle est proposé. Lors d’essais de marche (n=60), la justesse moyenne de l’orientation à la cheville a ainsi été améliorée de 6.7° à 2.8º. Finalement, la caractérisation de la signature de la cinématique tête-tronc pendant une tâche de retournement (variables : angle maximal tête-tronc, amplitude des commandes neuromusculaires) a démontré un bon pouvoir discriminant auprès de participants âgés sains (n=15) et de patients atteints de Parkinson (PD, n=15). Ces métriques ont également démontré une bonne sensibilité au changement, permettant l’identification des différents états de médication des participants PD. Conclusion : Les mesures d’orientation issues de CI ont leur place pour l’évaluation de la mobilité. Toutefois, la portée clinique réelle de ce type de système ne sera atteinte que lorsqu’il sera intégré et validé à même un outil de mesure clinique.Abstract : Inertial measurement of motion is emerging as an alternative to 3D motion capture systems in biomechanics. Inertial measurement units (IMUs) are composed of accelerometers, gyroscopes and magnetometers which data are fed into a fusion algorithm to determine the orientation of a rigid body in a global reference frame. Although IMUs offer advantages over traditional methods of motion capture, the value of their orientation measurement for biomechanics is not well documented. Objectives: (i) To characterize the validity of the orientation measurement issued from IMUs; (ii) To optimize the validity and the reliability of these measurements; and (iii) To propose mobility metrics based on the orientation measurement obtained from IMUs. Methods and results: The criterion of validity of multiple types of IMUs was characterized using a controlled bench test and a gold standard. Accuracy of orientation measurement was shown to be acceptable under slow conditions of motion (mean accuracy ≤ 3.1º), but it was also demonstrated that an increase in velocity worsens accuracy. The impact of those findings on clinical mobility evaluation was then assessed in the lab, with 20 participants wearing an inertial suit while performing typical mobility tasks (standing-up, walking, turning). Comparison of the assessed IMUs orientation measurements with those from an optical gold standard allowed to capture a portrait of the variation in accuracy across tasks, segments and joints. The optimization process was then approached from a user perspective, first demonstrating the capability of an artificial neural network to autonomously assess the quality of orientation data sequences (sensitivity and specificity ≥ 83%). The issue of joint orientation accuracy in magnetically perturbed environment was also specifically addressed, demonstrating the ability of a 2D photograph coupled with a 3D pose estimation algorithm to improve mean ankle orientation accuracy from 6.7° to 2.8º when walking (n=60 trials). Finally, characterization of the turn cranio-caudal kinematics signature (variables: maximum head to trunk angle and neuromuscular commands amplitude) has demonstrated a good ability to discriminate between healthy older adults (n=15) and early stages of Parkinson’s disease patients (PD, n=15). Metrics have also shown a good sensitivity to change, enabling to detect changes in PD medication states. Conclusion: IMUs offer a complementary solution for mobility assessment in clinical biomechanics. However, the full potential of this technology will only be reached when IMUs will be integrated and validated within a clinical tool

Feasibility and Reliability of a Commercially Available Stretch-Sensitive Sensor for Neck Movement

The ability to move the neck is usually a good indicator of neck health. However, the tools currently available to measure neck range of motion rely on gravity and the clinician\u27s ability to accurately line the instruments on specific landmarks of the body. This study explored whether a commercially available wearable sensor, C-Stretch® that is flexible and lightweight can capture the functional performance of cervical motion across testing sessions. Furthermore, an assessment of the C-Stretch® against Aurora NDI, an electromagnetic tracking system was explored to determine the feasibility of transforming raw capacitance data into degrees of motion. Finally, a survey explored the user’s experience with C-Stretch®. The C-Stretch® was able to monitor cervical motion across testing with good reliability for the Bag-Lift and poor reliability for the Bag-Slide and Star task (ICC2,1 0.57, 0.39, 0.37), respectively. The systems accuracy and agreement for rotational neck motion were evaluated. The C-Stretch® showed high correlation (r = 0.90-0.99, p \u3c 0.01) for areas of overlap and was accurate for both sessions with average RMSE values of 5.06° (95% C.I = 0.30° to 10.10°) for the first session and 5.34° (95% C.I = 0.10° to 10.79°) for the second session with respect to the electromagnetic tracking system. Overall, users tolerated the C-Stretch® and did not find it uncomfortable. This study highlights the feasibility of using wearable stretch sensors that are light, unobtrusive and comfortable for assessing functional performance of the cervical spine