Magnetic and radar sensing for multimodal remote health monitoring

With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained

Inertial data-based AI approaches for ADL and fall recognition

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.FCT—Fundação para a Ciência e Tecnologia—national funds, under the scholarship references UMINHO-VC/BII/2021/03 and PD/BD/141515/2018, and the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/202

Automatic Assessment of Medication States of Patients with Parkinson’s Disease using Wearable Sensors

Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson\u27s disease (PD). In this thesis, an automated algorithm is developed to identify those fluctuations (i.e., medication OFF and ON) using wearable sensors while PD patients are engaging in a variety of daily living activities. Four different methods are proposed which are supervised learning using Support Vector machine (SVM) with fuzzy classification, semi-supervised learning using k-means or using Self-organizing Tree Map Algorithm (SOTM) with fuzzy classification, and supervised classification using Long short-term memory (LSTM) as a deep learning method. A set of temporal and spectral features are extracted from the ambulatory signals of triaxial gyroscope sensors. After performing dimensionality reduction, the features are introduced to SVM or clustering methods using k-means or SOTM. Signals of the gyroscope sensors are passed directly to LSTM network. The developed methods were evaluated on two datasets that included recordings of 19 PD patients. Two scenarios were considered: general training/classification and patient-specific where the former trains and tests the algorithm using subject-based leave-one-out cross-validation, and the latter trains and tests the algorithm for each patient individually. In addition, for patient-specific scenarios, the number and placement of sensors is selected for each patient and this selection is based on the average change in UPDRS score between ON and OFF medication states and the presence of tremor for that patient. Overall, patient-specific algorithm resulted in a higher classification performance when it based on SVM with fuzzy classification (i.e., 80%, 82% and 78% for accuracy, sensitivity, and specificity of the OFF state, respectively). This algorithm was able to classify the medication states with high confidence (i.e., accuracy 94.86%, sensitivity 91.94% and specificity 96.83%) for the group of patients with a change of more than 15 in their UPDRS score between the OFF and ON medication states. This results are promising and thus this algorithm can be potentially used in routine clinical practice to improve the quality of this group of PD patients. In addition to these results, when only one sensor mounted on the ankle was used in the general training scenario, the algorithm based on LSTM performed better than SVM with 74.91%, 69.42%, and 80.55% for accuracy, sensitivity, and specificity, respectively. The promising results of LSTM show the potential outcome of developing deep learning methods in this field

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Cross-dataset evaluation of wearable fall detection systems using data from real falls and long-term monitoring of daily life

The evaluation of fall detection systems based on wearables is controversial as most studies in the literature benchmark their proposals against falls that are simulated by experimental subjects under unrealistic laboratory conditions. In order to systematically investigate the suitability of this procedure, this paper evaluates a wide set of artificial intelligence algorithms used for fall detection, when trained with a large number of datasets containing acceleration samples captured during the emulation of falls and ordinary movements and then tested with the signals of both actual falls and long-term traces collected from the constant monitoring of users during their daily routines. The results, based on a large number of repositories, show a remarkable degradation in all performance metrics (sensitivity, specificity and false alarm hourly rate) with respect to the typical case in which the detectors are tested with the same types of laboratory movements for which they were trained.Funding for open access charge: Universidad de Málaga / CBU

Efficient information distribution in the Internet of Medical Things (IoMT)

Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods.Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods

Frailty assessment based on trunk kinematic parameters during walking

Background: Physical frailty has become the center of attention of basic, clinical and demographic research due to its incidence level and gravity of adverse outcomes with age. Frailty syndrome is estimated to affect 20 % of the population older than 75 years. Thus, one of the greatest current challenges in this field is to identify parameters that can discriminate between vulnerable and robust subjects. Gait analysis has been widely used to predict frailty. The aim of the present study was to investigate whether a collection of parameters extracted from the trunk acceleration signals could provide additional accurate information about frailty syndrome. Methods: A total of 718 subjects from an elderly population (319 males, 399 females; age: 75.4 ± 6.1 years, mass: 71.8 ± 12.4 kg, height: 158 ± 6 cm) volunteered to participate in this study. The subjects completed a 3-m walk test at their own gait velocity. Kinematic data were acquired from a tri-axial inertial orientation tracker. Findings: The spatio-temporal and frequency parameters measured in this study with an inertial sensor are related to gait disorders and showed significant differences among groups (frail, pre-frail and robust). A selection of those parameters improves frailty classification obtained to gait velocity, compared to classification model based on gait velocity solely. Interpretation: Gait parameters simultaneously used with gait velocity are able to provide useful information for a more accurate frailty classification. Moreover, this technique could improve the early detection of pre-frail status, allowing clinicians to perform measurements outside of a laboratory environment with the potential to prescribe a treatment for reversing their physical decline.This work was supported in part by the Spanish Department of Health and Institute Carlos III of the Government of Spain [Spanish Net on Aging and frailty; (RETICEF)], and Economy and Competitivity Department of the Government of Spain, under grants numbered RD12/043/0002, and DEP2011-24105, respectively

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Cataloged from PDF version of article.Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account