Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology

Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario

An indoor localisation and motion monitoring system to determine behavioural activity in dementia afflicted patients in aged care

Dementia is highly prevalent among the older population. Most patients with dementia are admitted to an aged care facility due to wandering behaviour which tends to result in dangerous scenarios such as straying away from the facility and being seriously injured. Due to the decreasing availability of carers in aged care, there is a need to prioritise monitoring of patients that have a severe case of wondering. The challenge is to allow carers to monitor the status of such patients in terms of position localisation and motion behavioural status, in real-time. The long term behavioural analysis of such patients would allow carers to better manage such patients. Current indoor localisation technologies cannot provide the accuracy of location and motion to enable unobtrusive behavioural analysis. Our aim was to develop an indoor localisation and activity monitoring system for aged care workers to aid the prioritisation of surveillance to the patients with dementia. Our system used Radio Frequency tracking combined with motion and heading sensors to track a person. The motion and heading sensor information were incorporated into a human activity classification model to determine the characteristics of a patient\u27s walking activity. We conducted a month-long trial of our localisation network and activity monitoring system in an aged care facility

Use of mobile phone based health applications in home care delivery of cardiac rehabilitation

Cardiovascular disease is the leading cause of death and one of the greatest burdens to economies worldwide. Cardiac rehabilitation can effectively address risk factors prevalent to cardiovascular disease, as well as reduce morbidity and mortality. In spite of documented benefits, poor rates of referral, uptake and utilization of cardiac rehabilitation programs continue. The Care Assessment Platform (CAP) is an innovative home care model which provides an alternative delivery approach to rehabilitation for cardiac patients, utilising a mobile phone platform. This paper reports results on use and user acceptance of mobile phone applications utilised in the CAP model and evaluated in a randomized controlled trial. The implementation of mobile applications in the CAP model showed high usage and acceptance by patients (>85%). Mobile applications, such as a health diary and step counter, show promise in supporting self monitoring and management of lifestyle related health risk factors in the management of other chronic diseases

Review of accelerometry for determining daily activity among elderly patients

Cheung VH, Gray L, Karunanithi M. Review of accelerometry for determining daily activity among elderly patients. Arch Phys Med Rehabil 2011;92:998-1014