Julien Pansiot

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.

Swimming Stroke Kinematic Analysis with BSN

The recent maturity of body sensor networks has enabled a wide range of applications in sports, well-being and healthcare. In this paper, we hypothesise that a single unobtrusive head-worn inertial sensor can be used to infer certain biomotion details of specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmers goggles, limiting the disturbance to a minimum. Features extracted from the recorded acceleration such as the pitch and roll angles allow to recognise the type of stroke, as well as basic biomotion indices. The system proposed represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring.

Behaviour Profiling with Ambient and Wearable Sensing

This paper investigates the combined use of ambient and wearable sensing for inferring changes in patient behaviour patterns. It has been demonstrated that with the use of wearable and blob based ambient sensors, it is possible to develop an effective visualization framework allowing the observation of daily activities in a homecare environment. An effective behaviour modelling method based on Hidden Markov Models (HMMs) has been proposed for highlighting changes in activity patterns. This allows for the representation of sequences in a similarity space that can be used for clustering or data-exploration.

An integrated multi-sensing framework for pervasive healthcare monitoring

Pervasive healthcare provides an effective solution for monitoring the wellbeing of elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinsons. However, developing functional pervasive systems is a complex task that entails the creation of appropriate sensing platforms, integration of versatile technologies for data stream management and development of elaborate data analysis techniques. This paper describes a complete and an integrated multi-sensing framework, with which the sensing platforms, data fusion and analysis algorithms, and software architecture suitable for pervasive healthcare applications are presented. The potential value of the proposed framework for pervasive patient monitoring is demonstrated and initial results obtained from our current research experiences are described.

From computers to ubiquitous computing by 2010: health care

Over the past decade, miniaturization and cost reduction in semiconductors have led to computers smaller in size than a pinhead with powerful processing abilities that are affordable enough to be disposable. Similar advances in wireless communication, sensor design and energy storage have meant that the concept of a truly pervasive ‘wireless sensor network’, used to monitor environments and objects within them, has become a reality. The need for a wireless sensor network designed specifically for human body monitoring has led to the development of wireless ‘body sensor network’ (BSN) platforms composed of tiny integrated microsensors with on-board processing and wireless data transfer capability. The ubiquitous computing abilities of BSNs offer the prospect of continuous monitoring of human health in any environment, be it home, hospital, outdoors or the workplace. This pervasive technology comes at a time when Western world health care costs have sharply risen, reflected by increasing expenditure on health care as a proportion of gross domestic product over the last 20 years. Drivers of this rise include an ageing post ‘baby boom’ population, higher incidence of chronic disease and the need for earlier diagnosis. This paper outlines the role of pervasive health care technologies in providing more efficient health care.

Body Sensor Networks for Monitoring Rowing Technique

This paper presents a prototype for monitoring the kinematics of the femur and lower back (sacrum and thoraco-lumbar junction) during rowing. Data is collected from inertial sensors attached to the rower and a simple yet relatively accurate method for determining the rotation of the lower back and femur in the sagittal plane is presented. We also present results from an initial validation experiment using an optical tracking system which demonstrate that it is possible to monitor rowers using the proposed sensors and identify some common poor rowing techniques. Due to their small size, wireless capability and lightweight characteristics, the proposed Body Sensor Network (BSN) system has the potential to be used during ergometer sessions and whilst training on the water.

ClimBSN: Climber performance monitoring with BSN

The recent growth in popularity in sport climbing is partly due to the safe environment provided by indoor climbing walls, particularly for novice climbers. Sport climbing involves a wide range of skills and abilities. The purpose of this paper is to present a wearable sensing platform and an analysis framework for assessing general climbing performance during training. To provide the required freedom of movement, a single miniaturized ear-worn 3D accelerometer-based sensor is used. Independent features derived from the accelerometer data are then translated into climbing-specific measures, such as motion fluidity, strength, as well as endurance. Based on these indices, the overall level of the climber and the associated climbing styles can be quantified.

Bayesian Analysis of Sub-plantar Ground Reaction Force with BSN

The assessment of Ground Reaction Forces (GRF) is important for gait analysis for sports, pathological gaits and rehabilitation. To capture GRF, force plates and foot pressure insoles are commonly used. Due to cost and portability issues, such systems are mostly limited to lab-based studies. Long-term, continuous and pervasive measurement of GRF is not feasible. This paper presents a novel concept of using an ear-worn sensor for pervasive gait analysis. By emulating the human vestibular system, the bio-inspired design sensor effectively captures the shock wave generated by the GRF. A hierarchical Bayesian network is developed to estimate the plantar force distribution from the ear sensor signals. The accuracy of the ear sensor for detecting GRF is demonstrated by comparing the results with a high-accuracy commercial foot pressure insole system.

Wearable and ambient sensor fusion for the characterisation of human motion

Home monitoring plays an important role within pervasive healthcare, particularly for monitoring the elderly and patients with chronic disease. For assessing activities of daily living, one of the most challenging problems for research remains that of accurate transition detection and characterisation. Early detection of a change in these transitions, such as difficulty getting up from a seated position, can be an indicator of further complications which often precede a fall. Such changes can also accompany early stage neurological disorders which can be treated effectively to improve quality of life. In this paper, we present a system for the accurate characterisation of motion based upon the fusion of ambient and wearable sensors. A probabilistic, privacy respectful method for the extraction of detailed 3D posture information is proposed and fusion with an ear-worn accelerometer and gyroscope is discussed. We present results detailing high accuracy in the recognition of complex motions over four subjects.

WISDOM: wheelchair inertial sensors for displacement and orientation monitoring

Improved wheelchair design in recent years has significantly increased the mobility of people with disabilities, which has also enhanced the competitive advantage of wheelchair sports. For the latter, detailed assessment of biomechanical factors influencing individual performance and team tactics requires real-time wireless sensing and data modelling. In this paper, we propose the use of a miniaturized wireless wheel-mounted inertial sensor for wheelchair motion monitoring and tracking in an indoor sport environment. Based on a combined use of 3D microelectromechanical system (MEMS) gyroscopes and 2D MEMS accelerometers, the proposed system provides real-time velocity, heading, ground distance covered and motion trajectory of the wheelchair across the sports court. The proposed system offers a number of advantages compared to existing platforms in terms of size, weight and ease of installation. Beyond sport applications, it also has important applications for training and rehabilitation for people with disabilities.