Cheol-Hong Min

Detection of early morning daily activities with static home and wearable wireless sensors

This paper describes a flexible, cost-effective, wireless in-home activity monitoring system for assisting patients with cognitive impairments due to traumatic brain injury (TBI). The system locates the subject with fixed home sensors and classifies early morning bathroom activities of daily living with a wearable wireless accelerometer. The system extracts time- and frequency-domain features from the accelerometer data and classifies these features with a hybrid classifier that combines Gaussian mixture models and a finite state machine. In particular, the paper establishes that despite similarities between early morning bathroom activities of daily living, it is possible to detect and classify these activities with high accuracy. It also discusses system training and provides data to show that with proper feature selection, accurate detection and classification are possible for any subject with no subject specific training.

Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data

In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD). A microphone was used to record sounds so that we may understand the surrounding environment and video provided ground truth for analysis. The analysis was done on four children diagnosed with ASD who showed repeated self-stimulatory 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. The goal of this study is to devise novel algorithms to detect these events and open possibility for design of intervention methods. In this paper, we have shown time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events. We observe clusters of pole locations from LPC roots to select candidates and apply pattern matching for classification. We also show novel event detection using online dictionary update method. We show that our proposed method achieves recall rate of 95.5% for SIB, 93.5% for flapping, and 95.5% for rocking which is an increase of approximately 5% compared to flapping events detected by using wrist worn sensors in our previous study.

Optimal sensor location for body sensor network to detect self-stimulatory behaviors of children with autism spectrum disorder

In this study, we investigate various locations of sensor positions to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder (ASD). The study is focused on finding optimal detection performance based on sensor location and number of sensors. To perform this study, we developed a wearable sensor system that uses a 3 axis accelerometer. A microphone was used to understand the surrounding environment and video provided ground truth for analysis. The recordings were done on 2 children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and vocalization of non-word sounds. We used time-frequency methods to extract features and sparse signal representation methods to design over-complete dictionary for data analysis, detection and classification of these ASD behavioral events. We show that using single sensor on the back achieves 95.5% classification rate for rocking and 80.5% for flapping. In contrast, flapping events can be recognized with 86.5% accuracy using wrist worn sensors.

Novel pattern detection in children with Autism Spectrum Disorder using Iterative Subspace Identification

Recent increase in the number of Autism cases has triggered an alarm in our society. Lack of effective diagnostics, interventions and associated cost makes early intervention and long term treatment difficult. In this paper, we describe novel methods to assist management by automatically detecting stereotypical behavioral patterns using accelerometer data. We use the Iterative Subspace Identification (ISI) algorithm to learn subspaces in which the sensor data lives. It extracts orthogonal subspaces which are used to generate dictionaries for clustering and for signal representation. It is also applied to detecting segments from acoustics data. We further improve the algorithm by detecting novel events which were not known to the system during the training. Using these methods, we achieved an average of 83% and 90% of classification rates for flapping and rocking behaviors and 93% for novel behavioral patterns studied in this paper.

Integration of Wearable Wireless Sensors and Non-Intrusive Wireless in-Home Monitoring System to Collect and Label the Data from Activities of Daily Living

We describe an inexpensive in-home monitoring system designed to assist patients with traumatic brain injuries plan and execute daily activities. The system consists of fixed and wearable wireless sensors, including motion, pressure, door, flow, accelerometer, magnetometer, temperature, light and sound sensors. The sensors provide information that can be used to localize the patient, detect the activity they are engaged in and interruptions that may prevent them from completing the activity in a timely manner. During the system design and training phase, we augment the system with time stamped video and audio collection to provide a ground truth and label training data.

Semi-supervised event detection using higher order statistics for multidimensional time series accelerometer data

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.

In-Home Assistive System for Traumatic Brain Injury Patients

We describe a system for assisting patients in a home setting who suffer from cognitive impairments due to traumatic brain injury. The system integrates fixed wireless home sensors and wearable wireless sensors. We focus on the task of classifying activities of daily living. We locate and track the subjects with the help of home sensors and capture the details of an executed activity with a 2-axis wearable wireless accelerometer sensor attached to the right wrist. We extract time domain and frequency domain features for each task and classify them with Gaussian mixture models followed by a majority voter. The majority voter provides low false positive rates while continuously tracking the tasks. The experimental results from 2 subjects in recognizing 4 distinct daily activity tasks are promising.

Lower-limb goniometry using stitched sensors: effects of manufacturing and wear variables

Smart fabrics allow for convenient wearable sensing solutions to monitor body movements during our daily life. However, garment-integrated sensing presents challenges for accurate sensing, due many variables including those presented by variability in garment and sensor dimensions due to cut-and-sew manufacturing processes, and those introduced by re-positioning of integrated sensors when the garment is donned and doffed. Here, we measure the effect of variability in garment positioning due to donning and doffing, garment dimension due to manufacturing tolerances, and sensor dimension due to manufacturing defects on the accuracy of a stitched goniometer used to measure flexion of the knee and hip. Results show that variability in garment positioning and garment dimension have a minimal effect on sensor accuracy, but sensor dimensions have a more significant influence on accuracy.

Detection of Self-Stimulatory Behaviors of Children with Autism Using Wearable and Environmental Sensors

Autism is one of the five pervasive development disorders that may cause severe impairment to a child. Depending on the degree of the symptoms, autism may cause severe impairments in ones social life such as social interaction and communication with other individuals. They may also face challenges in learning, concentrating, sensation and interacting with their surroundings. According to the Center for Disease Control (CDC), 1 in 150 8-year old children in many areas in the United States were diagnosed with autism. It is also known from recent studies that with early diagnosis we can intervene earlier which allows better assistance and treatment. Therefore, it is critical to have an objective assessment tool to assist diagnosis and for management. We have developed an affordable, reliable system that provides evidence based tools for assessment of children with autism. This system can detect various repetitive behavioral patterns often seen in children with autism and enables long term monitoring of repetitive behaviors. Therefore, it can be used to assist doctors, therapists, caregivers and parents with diagnosis and treatment of children with autism. This system incorporates 2 different sensor platforms which include environmental and wearable sensors. The system consists of a 3-axis accelerometer, small microcontroller and a Bluetooth module to transmit data to a base station such as a PC for analysis. We have customized this wearable device to integrate these modules which can be worn by a child. The environmental sensor configuration is composed of a microphone which records the acoustic data of the subject within the room. Using this sensor system, we are able to achieve the necessary information for assessment and therapy in autism research. We have analyzed the 3-axis accelerometer and acoustic data with an intelligent machine learning algorithm. The algorithm extracts time-domain and frequency domain features from the accelerometer data and applies statistical learning techniques to detect repetitive behavioral patterns. For acoustic data, we used sparse signal representation techniques to detect repetitive patterns that indicate vocalization behaviors. We have achieved an average of 89% in classification accuracy for detecting behavioral patterns. Based on the real data collected from children with autism, we were able to detect and recognize four self-stimulatory behaviors of children with autism. In one instance in which a subject had a tantrum, using the correlation between the hand flapping ratio and vocalization intensity, we were able to predict this extreme behavior. Our study opens an application in which devices could be used in a classroom environment to predict extreme behaviors in order that the stress of children with autism could be diverted accordingly so that their actions would be more socially agreeable.