Victor Foo Siang Fook

Smart mote-based medical system for monitoring and handling medication among persons with dementia

This paper presents a novel smart mote-based portable medical system which automatically monitors and handles medication among persons with dementia based on wireless multimodal sensors, actuators and mobile phone or PDA (Personal Digital Assistance) technology. In particular, we present the subtle design, implementation and deployment issues of monitoring the patients behavior and providing adaptive assistive intervention such as prompts or reminders in the form of visual, audio or text cues to the patient for medical compliance. In addition, we develop mobile phone or PDA applications to provide a number of novel services to the caregivers that facilitate them in care-giving and to doctors for clinical assessment of dementia patients in a context enlightened fashion.

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. This paper presents the design, development, and preliminary deployment of a smart wireless continence management system for dementia-impaired elderly or patients in institutional care settings such as nursing homes and hospitals. Specifically, the mote wireless platform was used to support the deployment of potentially large quantities of wetness sensors with wider coverage and with dramatically less complexity and cost. It consists of an intelligent signal relay mechanism so that the residents are free to move about in the nursing home or hospital and allows personalized continence management service. Preliminary results from a trial in a local nursing home are promising and can significantly improve the quality of care for patients.

Automatic heart rate detection from FBG sensors using sensor fusion and enhanced empirical mode decomposition

Cardiovascular diseases are the worlds top leading causes of death. Real time monitoring of patients who have cardiovascular abnormalities can provide comprehensive and preventative health care. We investigate the role of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sensor fusion for automatic heart rate detection from a mat with embedded Fiber Bragg Grating (FBG) sensor arrays. The fusion process is performed in the time domain by averaging the readings of the sensors for each sensor array. Subsequently, the CEEMDAN is applied to obtain the interbeat intervals. Experiments are performed with 10 human subjects (males and females) lying on two different positions on a bed for a period of 20 minutes. The overall system performance is assessed against the reference ECG signals. The average and standard deviation of the mean relative absolute error are 0.049, 0.019 and 0.047, 0.038 for fused and best sensors respectively. Sensor fusion together with CEEMDAN proved to be robust against motion artifacts caused by body movements.

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. Experiments were conducted with 10 human subjects lying on 2 different postures on a bed. The estimated heart rate from BCG was compared with heart rate ground truth from ECG, and the mean error of estimation obtained is below 1 beat per minute (BPM). The results show that the proposed fusion method can achieve promising heart rate measurement accuracy and robustness against various sensor contact conditions.

Service Oriented Architecture for Patient Monitoring Application

This paper presents a new approach that exploits service oriented architecture (SOA) for patient monitoring application. In particular, we describe the use of web services, UPnP and semantic web in a loosely coupled manner to monitor dementia patients, model sensors and pervasive devices as services, and enable proactive action for handling patients by automatically triggering intervention services. The proposed architecture enables the development of a sophisticated monitoring system to facilitate care-giving and clinical assessment of patients in a context enlightened fashion within an administrative domain and across the internet that exceeds the current state-of-the-art in terms of scalability, interoperability, intelligence and robustness.

Data Collection and Feature Extraction for a Smart Ward Application

Agitated behavior is common in people with dementia, and one of the challenges faced by doctors and caregivers is the detailed and continuous monitoring of such people. With the use of modern sensor and networking technology, and system directed observation, objective outcomes may be expected and with removal of the manpower requirement. This paper presents a context aware patient data collection system (CAPDCS), which is done on the fly with multiple sensors of different modalities deployed in the hospital ward. These features extracted from each sensor modality are used in data fusion algorithms. We also present an approach for multi-modalities data fusion based on Bayesian network.

iCOPE: Intelligent Context-Aware Patient Management Systems for Elderly with Cognitive and Functional Impairment

This paper presents the design, development and features of iCOPE, a collection of innovative patient management systems that exploit the advances in context aware computing to monitor and detect highly complex behaviour of elderly with cognitive and functional impairment, and facilitate informal care-giving, therapy and clinical assessment in a context enlightened fashion. In particular, we describe the layered framework, methodologies, and application features such as continence management, agitation rating, medication management, sleep activity monitoring, telephysiotherapy and respiratory monitoring to illustrate the possibilities offered by iCOPE. A novel service oriented intervention and just-in-time therapy mechanism using uPnP which can handle both synchronous and asynchronous events is also presented. iCOPE can promote independent living and improve quality of life for elderly persons living alone, and can provide caregivers and family members with peace of mind as they know that there are systems in place to monitor their loved ones. In an institutional or nursing home setting, iCOPE can provide valuable backup since it operates round the clock and has the potential to identify anomalous behavior that might otherwise be missed which in turn can improve the quality of care for patients. It is hoped that iCOPE will extend the capabilities of monitoring to various settings, thereby encouraging the clinicians and caregivers to examine new interventions and therapy that might improve the patients’ overall condition.

Fusion Considerations in Monitoring and Handling Agitation Behaviour for Persons with Dementia

This paper presents the fusion considerations for a smart hospital application. In particular, we present the subtle design and implementation of fusion architecture for monitoring and handling agitation behaviour for persons with dementia. In addition, we exploit semantic Web standards to provide a reusable fusion middleware support for providing services that facilitate care-giving and clinical assessment of dementia patients in a context enlightened fashion

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. The method estimates the quality of the signal waveform by detecting regularly repetitive patterns using proposed spectrum and cepstrum analysis. Multiple window sizes are used to cater for a wide range of target respiratory rates. Furthermore, the readings of multiple sensors are fused to derive a final respiratory rate. Experiments with 12 subjects and 2 body positions were conducted using polysomnography belt signal as groundtruth. The results demonstrated the effectiveness of the method.