Classification of Handwashing and Similar Activities.

In this paper, an ensemble gentle boost decision tree classification algorithm is trained to classify handwashing from similar activities such as applying lotion to hands. Data is collected using a 3-axis accelerometer and gyroscope worn on the wrist. First, the data collection procedure is described.

Fall Prediction in People with Parkinson's Disease.

A preliminary study result predicting fall events in patients with Parkinson's disease (PD) by using a simple motion sensor is described in this paper. Causes of falls in people with PD can be postural instability, freezing of gait, festinating gait, dyskinesias, visuospatial dysfunction, orthostatic hypotension, and posture problems. This study uses only one motion sensor in collecting data.

Deciphering clock cell network morphology within the biological master clock, suprachiasmatic nucleus: From the perspective of circadian wave dynamics.

The biological master clock, suprachiasmatic nucleus (of rat and mouse), is composed of ~10,000 clock cells which are heterogeneous with respect to their circadian periods. Despite this inhomogeneity, an intact SCN maintains a very good degree of circadian phase (time) coherence which is vital for sustaining various circadian rhythmic activities, and it is supposedly achieved by not just one but a few different cell-to-cell coupling mechanisms, among which action potential (AP)-mediated connectivity is known to be essential. But, due to technical difficulties and limitations in experiments, so far very little information is available about the morphology of the connectivity at a cellular scale.

Garment Integrated Spinal Posture Detection Using Wearable Magnetic Sensors.

Spine Curvature Disorder (SCD) is a medical condition that affects the shape of the spine. Methods of monitoring SCDs involve visual inspection followed by X-rays and measurements. Once a patient is diagnosed with SCD and treatment or therapy is implemented, progress is tracked by exposing the patient to multiple periodic X-rays to determine the spine responses to treatments or therapies.

Training a Neural Network for Vocal Stereotypy Detection.

This system was developed to detect and diagnose vocal stereotypies made by non-verbal autistic children. Vocal stereotypies are loud non-speech vocalizations made by these children.The system discussed in this paper uses a deep learning neural network to detect these vocalizations.

Diversity in the structure of action potential-mediated neural connectivity within rat supra chiasmatic nucleus.

Action potential (AP)-mediated cell-to-cell communication is essential for the frequency-locking and phase-synchronization of the clock cells within the biological master clock, suprachiasmatic nucleus (SCN). Nevertheless, the morphology of its network connectivity is largely unexplored. Here, with an optimized optogenetic light-stimulation and scanning protocol, we report some key characteristics of the inhibitory receptive field (IRF), the area which brings inhibitory synaptic currents to a given target cell, and basic statistics of the inhibitory network connections of rat SCN clock cells.

Vocal Stereotypy Detection: An Initial Step to Understanding Emotions of Children with Autism Spectrum Disorder.

A system has been developed to automatically record and detect behavioral patterns and vocal stereotypy which is also known as vocal stimming, a non-verbal vocalization often observed in children with Autism Spectrum Disorder (ASD). The system incorporates audio, video and wearable accelerometer based sensors. Microphones and video camera were used to collect data and were used for analysis.

Automatic detection and labeling of self-stimulatory behavioral patterns in children with Autism Spectrum Disorder.

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.

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.

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).

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.

Classification of continuously executed early morning activities using wearable wireless sensors.

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.