Sensor data quality processing for vital signs with opportunistic ambient sensing.

Opportunistic ambient sensing involves placement of sensors appropriately so that intermittent contact can be made unobtrusively for gathering physiological signals for vital signs. In this paper, we discuss the results of our quality processing system used to extract heart rate from ballisto-cardiogram signals obtained from a micro-bending fiber optic sensor pressure mat. Visual inspection is used to label data into informative and non-informative classes based on their heart rate information.

Estimating respiratory rate from FBG optical sensors by using signal quality measurement.

Non-intrusiveness is one of the advantages of in-bed optical sensor device for monitoring vital signs, including heart rate and respiratory rate. Estimating respiratory rate reliably using such sensors, however, is challenging, due to body movement, signal variation according to different subjects or body positions, etc. This paper presents a method for reliable respiratory rate estimation for FBG optical sensors by introducing signal quality estimation.

Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion.

This paper presents a method of estimating heart rate from arrays of fiber Bragg grating (FBG) sensors embedded in a mat. A cepstral domain signal analysis technique is proposed to characterize Ballistocardiogram (BCG) signals. With this technique, the average heart beat intervals can be estimated by detecting the dominant peaks in the cepstrum, and the signals of multiple sensors can be fused together to obtain higher signal to noise ratio than each individual sensor.

Detection of variations in cognitive workload using multi-modality physiological sensors and a large margin unbiased regression machine.

Physiological sensor based workload estimation technology provides a real-time means for assessing cognitive workload and has a broad range of applications in cognitive ergonomics, mental health monitoring, etc. In this paper we report a study on detecting changes in workload using multi-modality physiological sensors and a novel feature extraction and classification algorithm. We conducted a cognitive workload experiment involving multiple subjects and collected an extensive data set of EEG, ECG and GSR signals.

Ballistocardiography with fiber optic sensor in headrest position: a feasibility study and a new processing algorithm.

Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor.

Processing of wearable sensor data on the cloud - a step towards scaling of continuous monitoring of health and well-being.

As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and the processing of the data was done offline after sleep diaries used for validation and ground truth were updated into the system.

Sleeping patterns observation for bedsores and bed-side falls prevention.

Disabled or cognition impaired elderly may lie in the bed most of their time. It is important to monitor their health conditions and look out for life threatening events in and around the bed continuously. Abrupt unassisted movements may lead to falls whereas the lack of desirable movements may cause bedsores.

Fast matching of sensor data with manual observations.

In systems and trials concerning wearable sensors and devices used for medical data collection, the validation of sensor data with respect to manual observations is very important. However, this is often problematic because of feigned behavior, errors in manual recording (misclassification), gaps in recording (missing readings), missed observations and timing mismatch between manual observations and sensor data due to a difference in time granularity. Using sleep activity pattern monitoring as an example we present a fast algorithm for matching sensor data with manual observations.

Smart wireless continence management system for persons with dementia.

Incontinence is highly prevalent in the elderly population, especially in nursing home residents with dementia. It is a distressing and costly health problem that affects not only the patients but also the caregivers. Effective continence management is required to provide quality care, and to eliminate high labor costs and annoyances to the caregivers resulting from episodes of incontinence.