Benny Lo

Unobtrusive Sensing and Wearable Devices for Health Informatics

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

Sensor Positioning for Activity Recognition Using Wearable Accelerometers

Activities of daily living are important for assessing changes in physical and behavioral profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers have been used widely in wearable devices for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose significant research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a systematic framework that can answer the following questions: 1) What is the ideal sensor location for a given group of activities? and 2) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are the most relevant for discriminating different activity types?

Sensor Placement for Activity Detection Using Wearable Accelerometers

Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a framework that can answer the following questions: (i) What is the ideal sensor location for a given group of activities? (ii) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are most relevant for discriminating different activity types?

Deep learning for health informatics

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor

Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decreasing the reaction time by the care-givers especially for those in care who are particularly frail or living alone. Recent advances in motion-sensor technology have enabled wearable sensors to be used efficiently for pervasive care of the elderly. In addition to fall detection, it is also important to determine the direction of a fall, which could help in the location of joint weakness or post-fall fracture. This work uses a waist-worn sensor, encompassing a 3D accelerometer and a barometric pressure sensor, for reliable fall detection and the determination of the direction of a fall. Also assessed is an efficient analysis framework suitable for on-node implementation using a low-power micro-controller that involves both feature extraction and fall detection. A detailed laboratory analysis is presented validating the practical application of the system.

Real-Time Activity Classification Using Ambient and Wearable Sensors

New approaches to chronic disease management within a home or community setting offer patients the prospect of more individually focused care and improved quality of life. This paper investigates the use of a light-weight ear worn activity recognition device combined with wireless ambient sensors for identifying common activities of daily living. A two-stage Bayesian classifier that uses information from both types of sensors is presented. Detailed experimental validation is provided for datasets collected in a laboratory setting as well as in a home environment. Issues concerning the effective use of the relatively limited discriminative power of the ambient sensors are discussed. The proposed framework bodes well for a multi-dwelling environment, and offers a pervasive sensing environment for both patients and care-takers.

Multichannel Reflective PPG Earpiece Sensor With Passive Motion Cancellation

This paper addresses the design considerations of a novel earpiece photoplethymograph (PPG) sensor and its in-situ evaluation results. The device is encapsulated with multiple LEDs and photodiodes based on a reflective PPG design. A compact and low power circuitry was developed for signal control and conditioning. PPG signals with an averaged ac/dc ratio of 0.001-0.01 and 10% relative strength (compared to finger-based approach) were recorded from the superior and posterior auricular skins. The integrity of PPG signal and accuracy of heart rate detection were evaluated and the results showed that with adequate optical shielding and the proposed passive motion cancellation, the device was able to reliably detect heart rate both during rest and moderate exercise. The proposed sensor design is low power, easy to wear compared to conventional earlobe PPG devices.

A Pervasive Body Sensor Network for Measuring Postoperative Recovery at Home

Patients going home following major surgery are susceptible to complications such as wound infection, abscess formation, malnutrition, poor analgesia, and depression, all of which can develop after the fifth postoperative day and slow recovery. Although current hospital recovery monitoring systems are effective during perioperative and early postoperative periods, they cannot be used when the patient is at home. Measuring and quantifying home recovery is currently a subjective and labor-intensive process. This case report highlights the development and piloting of a wireless body sensor network to monitor postoperative recovery at home in patients undergoing abdominal surgery. The device consists of wearable sensors (vital signs, motion) combined with miniaturized computers wirelessly linked to each other, thus allowing continuous monitoring of patients in a pervasive (unobtrusive) manner in any environment. Initial pilot work with results in both the simulated (with volunteers) and the real home environment (with patients) is presented.

Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems

The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone.

Pervasive body sensor network: an approach to monitoring the post-operative surgical patient

Patients recovering from abdominal surgery are at risk of complications due to reduced mobility as a result of post-operative pain. The ability to pervasively monitor the recovery of this group of patients and identify those at risk of developing complications is therefore clinically desirable, which may result in an early intervention to prevent adverse outcomes. This paper describes the development and evaluation of a pervasive network of body sensors developed for monitoring the recovery of post-operative patients both in the hospital and homecare settings