A Wearable Sensing Framework for Improving Personal and Oral Hygiene for People with Developmental Disabilities

People with developmental disabilities often face difficulties in coping with daily activities and many require constant support. One of the major health issues for people with developmental disabilities is personal hygiene. Many lack the ability, poor memory or lack of attention to carry out normal daily activities like brushing teeth and washing hands. Poor personal hygiene may result in increased susceptibility to infection and other health issues. To enable independent living and improve the quality of care for people with developmental abilities, this paper proposes a new wearable sensing framework to monitoring personal hygiene. Based on a smartwatch, this framework is designed as a pervasive monitoring and learning tool to provide detailed evaluation and feedback to the user on hand washing and tooth brushing. A preliminary study was conducted to assess the performance of the approach, and the results showed the reliability and robustness of the framework in quantifying and assessing hand washing and tooth brushing activities

Adaptive Bayesian networks for video processing

ABSTRACT Due to its static nature, the inference capability of Bayesian Networks (BNs) oflen deteriorates when the basis of input data varies, especially in video processing applications where the environment often changes constantly. This paper presents an adaptive BN where the network parameters are adjusted in accordance to input variations. An efficient re-training method is introduced for updating the parameters and the proposed network is applied to shadow removal in video sequence processing with quantitative results demonstrating the significance of adapting the network with environmental changes

Wireless Wearable Photoplethysmography Sensors for Continuous Blood Pressure Monitoring

Blood Pressure (BP) is a crucial vital sign taken into consideration for the general assessment of patient’s condition: patients with hypertension or hypotension are advised to record their BP routinely. Particularly, hypertension is emphasized by stress, diabetic neuropathy and coronary heart diseases and could lead to stroke. Therefore, routine and long-term monitoring can enable early detection of symptoms and prevent life-threatening events. The gold standard method for measuring BP is the use of a stethoscope and sphygmomanometer to detect systolic and diastolic pressures. However, only discrete measurements are taken. To enable pervasive and continuous monitoring of BP, recent methods have been proposed: pulse arrival time (PAT) or PAT difference (PATD) between different body parts are based on the combination of electrocardiogram (ECG) and photoplethysmography (PPG) sensors. Nevertheless, this technique could be quite obtrusive as in addition to at least two contacts/electrodes to measure the differential voltage across the left arm/leg/chest and the right arm/leg/chest, ECG measurements are easily corrupted by motion artefacts. Although such devices are small, wearable and relatively convenient to use, most devices are not designed for continuous BP measurements. This paper introduces a novel PPG-based pervasive sensing platform for continuous measurements of BP. Based on the principle of using PAT to estimate BP, two PPG sensors are used to measure the PATD between the earlobe and the wrist to measure BP. The device is compared with a gold standard PPG sensor and validation of the concept is conducted with a preliminary study involving 9 healthy subjects. Results show that the mean BP and PATD are correlated with a 0.3 factor. This preliminary study shows the feasibility of continuous monitoring of BP using a pair of PPG placed on the ear lobe and wrist with PATD measurements is possible

Point2Volume: A vision-based dietary assessment approach using view synthesis

Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation

Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection

Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers

Design and prototyping of a bio-inspired kinematic sensing suit for the shoulder joint: precursor to a multi-DoF shoulder exosuit

Soft wearable robots represent a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multiple degrees-of-freedom shoulder exosuit capable of sensing the kinematics of the joint. The proposed sensing system is inspired by the body's embodied kinematic sensing, and the organisation of muscles and muscle synergies responsible for shoulder movements. A motion-capture-based evaluation study of the developed framework confirmed conformance with the behaviour of the muscles that inspired its routing. This validation of the tendon-routing hypothesis allows for it to be extended to the actuation framework of the exosuit in the future. The sensor-to-joint-space mapping is based on multivariate multiple regression and derived using an Artificial Neural Network. Evaluation of the derived mapping achieved root mean square error of ≈5.43° and ≃3.65° for the azimuth and elevation joint angles measured over 29,500 frames (4+ minutes) of motion-capture data

Introduction to the special issue on wearable and flexible integrated sensors for screening, diagnostics, and treatment

The papers in this special issue present a selection of high quality research papers on wearable and flexible integrated sensors for screening, diagnostics, and treatment. Emerging flexible and wearable physical sensing devices create huge potential for many vital healthcare and biomedical applications including artificial electronic skins, physiological monitoring and assessment systems, therapeutic and drug delivery platforms, etc. Monitoring of vital physiological parameters in hospital and/or home environments has been of tremendous interests to healthcare practitioners for a long time. Robust and reliable sensors with excellent flexibility and stretchability are essential in the development of pervasive health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous discomfort and invasiveness