In this paper, we study the personal monitoring system that classifies the continuously executed early morning activities of daily living. The system is intended to assist those with cognitive impairments due to traumatic brain injuries. The system can be used to help therapists in hospitals or could be deployed in one's home to track and monitor the activities executed by the recovering patients. We begin by briefly describing the infrastructure of our cost-effective system which uses fixed and wearable wireless sensors and show results related to the detection of activities continuously executed in the morning. Both frequency and time domain features from an accelerometer attached to the right wrist were extracted and used for classification using Gaussian mixture models, followed by a finite state machine. We show promising classification results obtained from 5 subjects. Overall classification rate is 88.3 % for 4 activities of interests.
In this study, we target to automatically detect stereotypical behavioral patterns (stereotypy) and self-injurious behaviors (SIB) of Autistic children which can lead to critical damages or wounds as they tend to repeatedly harm oneself. Our custom designed accelerometer based wearable sensors are placed at wrists, ankles and upper body to detect stereotypy and SIB. The analysis was done on four children diagnosed with ASD who showed repeated behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. Our goal of detecting novel events relies on the fact that the limitation of training data and variability in the possible combination of signals and events also make it impossible to design a single algorithm to understand all events in natural setting. Therefore, a semi-supervised method to discover and track unknown events in a multidimensional sensor data rises as a very important topic in classification and detection problems. In this paper, we show how the Higher Order Statistics (HOS) features can be used to design dictionaries and to detect novel events in a multichannel time series data. We explain our methods to detect novel events in a multidimensional time series data and combine the proposed semi-supervised learning method to improve the adaptability of the system while maintaining comparable detection accuracy as the supervised method. We, compare our results to the supervised methods that we have previously developed and show that although semi-supervised method do not achieve better performance compared to supervised methods, it can efficiently find new events and anomalies in multidimensional time series data with similar performance of the supervised method. We show that our proposed method achieves recall rate of 93.3% compared to 94.1% for the supervised method studied earlier.
An infrastructure to record, detect and label the behavioral patterns of children with Autism Spectrum Disorder (ASD) has been developed. The system incorporates 2 different sensor platforms which are wearable and static. The wearable system is based on accelerometer which detects behavioral patterns of a subject, while the static sensors are microphones and cameras which captures the sounds, images and videos of the subjects within a room. The video also provides ground truth for wearable sensor data analysis. The system labels the segment of video data upon detection of the autistic behavior. That is, it stores the time of the video when the activities are detected. Time-Frequency methods are used to extract features and Hidden Markov Model (HMM) are used for analyzing the accelerometer signal. Using these methods, we are able to achieve 91.5% of classification rate for behavioral patterns studied in this paper which is used to label and save data.
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