Rachel C. King

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?

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.

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

Development of a Wireless Sensor Glove for Surgical Skills Assessment

Laparoscopic surgery is a challenging task in minimally invasive surgery, which involves complex instrument control, extensive manual dexterity, and hand-eye coordination. This requires a greater attention to training and skills evaluation. In order to provide a more objective skills assessment method, this paper presents a wireless sensor platform for the capture of laparoscopic hand gesture data and a hidden-Markov-model-based analysis framework for optimal sensor selection and placement. Detailed experimental validation is provided to illustrate how the proposed method can be used to assess surgical performance improvement over repeated training.

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.

An HMM framework for optimal sensor selection with applications to BSN sensor glove design

Laparoscopic surgical training is a challenging task due to the complexity of instrument control and demand on manual dexterity and hand-eye coordination. Currently, training and assessing surgeons for their laparoscopic skills rely mainly on subjective assessment. This paper presents a Body Sensor Network (BSN) sensor glove for laparoscopic gesture recognition and objective assessment of surgical skills. An HMM framework is proposed for the selection of sensors to maximize the sensitivity and specificity of gesture recognition for a given set of laparoscopic tasks. With the proposed framework, the optimal location as well as the number of the sensors can be determined. The sensors used in this study include accelerometers and fiber optic bend sensors. Experimental data is collected by participants wearing the glove while performing simple laparoscopic tasks. By using the proposed HMM framework, sensor correlation and relevance to task recognition can be determined, thus allowing a reduction in the number of sensors used. Results have shown that it is possible to establish the intrinsic correlation of the sensors and determine which sensors are most relevant to specific gestures based on the proposed method.

Elderly Risk Assessment of Falls with BSN

Due to the natural aging process, the risks associated with falling can increase significantly. For the elderly, this usually marks a rapid deterioration of their health. While there are identified strategies that can be adopted to reduce the number of falls, it is still not possible to prevent all falls. Clinically, the Tinetti Gait and Balance Assessment has been widely used to assess the risk of falls in elderly by examining balance and gait. This paper presents our initial results of using an ear-worn BSN sensor to detect aspects of the Tinetti Gait and Balance Assessment to predict the risk of falls compared to a healthy control cohort. For this study, data was collected from a control cohort of 12 healthy volunteers and a cohort of 16 elderly fallers of varying degrees of risk. The results derived have shown that it is possible to directly detect some aspects of the Tinetti Gait and Balance Assessment and the Timed Up and Go test, demonstrating the potential value of using the platform for continuous assessment in a home environment.

Semi-supervised segmentation for activity recognition with Multiple Eigenspaces

Body Sensor Networks (BSNs) are increasingly being used in pervasive sensing environments including healthcare, sports, wellbeing, and gaming. Activity segmentation using BSN is challenging and the use of manual annotation is subjective and error prone. In this paper, we investigate a semi-supervised activity segmentation method using a Multiple Eigenspace (MES) technique based on Principal Components Analysis (PCA). Results show that the method can reliably perform activity segmentation and the classification results based on HMMs demonstrate the practical value of the proposed technique.