Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition

Abstract

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.

Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

“…Data collected during this study were analysed using an SDF architecture referred to as Modified Distributed Sensor Data Fusion and Evaluation Architecture (MDSFEA) [23], In Figure 1a, the red and the white spots indicate the location of the side-facing and the front-facing SS that were used to monitor the SPAREs. The yellow spot indicates where the participants sat during data collection.…”

“…The use of wearables such as triaxial accelerometers, gyroscopes, and inertial sensors to monitor and estimate the orientations of a body has been widely researched [12][13][14][15][16]. The sensors' usage has included measurement of the acceleration of the upper extremity post-stroke [17], indoor activity monitoring and classification [18][19][20][21][22][23], behaviour monitoring [24,25], and home-based motor functions rehabilitation [16], amongst others. In SPAREs, wearables such as foot-mounted inertial sensors have been used to estimate the orientation of the ankle [26].…”

Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

“…Data collected during this study were analysed using a sensor data fusion architecture referred to as Modified Distributed Sensor Data Fusion and Evaluation Architecture (MDSFEA) [15] as presented in Figure 2. The MDSFEA is an architecture suitable for data analysis ranging from homogeneous to heterogeneous datasets.…”

“…It differs from Linear Discriminant Analysis (LDA) because it is a variance-based algorithm, whereas LDA is based on class information [24]. Moreover, PCA is best suited for unsupervised data clustering, such as that used in this analysis [15].…”

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

“…Activity Recognition and Classification (ARC) through the use of mobile devices has also been researched [16]. Work by Figo et al [7] explored the use of a smartphone's accelerometer to recognise and classify activities such as running and walking during a certain period of the day.…”