Amaya Arcelus

Integration of Smart Home Technologies in a Health Monitoring System for the Elderly

Among older adults, the challenges of maintaining mobility and cognitive function make it increasingly difficult to remain living alone independently. As a result, many older adults are forced to seek residence in costly clinical institutions where they can receive constant medical supervision. A home-based automated system that monitors their health and well- being while remaining unobtrusive would provide them with a more comfortable and independent lifestyle, as well as more affordable care. This paper presents a smart home system for the elderly, developed by the Technology Assisted Friendly Environment for the Third Age (TAFETA) group. It introduces the sensor technologies integrated in the system and develops a framework for the processing and communication of the extracted information. It also considers the acceptability and implications of this technology from the perspective of the potential occupants.

Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences

The duration of a sit-to-stand (SiSt) transfer is a representative measure of a persons status of physical mobility. This paper measured the duration unobtrusively and automatically using a pressure sensor array under a bed mattress and a floor plate beside the bed. Pressure sequences were extracted from frames of sensor data measuring bed and floor pressure over time. The start time was determined by an algorithm based on the motion of the center of pressure (COP) on the mattress toward the front edge of the bed. The end time was determined by modeling the foot pressure exerted on the floor in the wavelet domain as the step response of a third-order transfer function. As expected, young and old healthy adults generated shorter SiSt durations of around 2.31 and 2.88 s, respectively, whereas post-hip fracture and post-stroke adults produced longer SiSt durations of around 3.32 and 5.00 s. The unobtrusive nature of pressure sensing techniques used in this paper provides valuable information that can be used for the ongoing monitoring of patients within extended-care facilities or within the smart home environment.

Measurements of Sit-to-Stand Timing and Symmetry From Bed Pressure Sensors

Sit-to-stand (SiSt) analysis has been widely used in clinical practice to assess the risk of falls in the older adult population. This paper proposes automated algorithms for the unobtrusive measurement of SiSt timing and symmetry using bed pressure sensors. An integrated signal comprising all of the sensor outputs was analyzed to measure both the bed-departure timing and the timing of three clinical phases within the transfer. Data collected in clinical trials, along with independent clinical video analysis, verified the success of the bed-departure timing algorithm with a mean error of 0.11 s. The phase measurement algorithm showed significant differences (p <; 0.001) between younger and older adults in Phases II and III of the transfers, comparing well with studies found in recent clinical literature. The sensor outputs were then used to form sequences of pressure images, and an automated region of interest (ROI) detection algorithm was designed to extract regional signals from the hips and the hands. The final algorithm was designed to measure the symmetry of the body throughout the SiSt transfer from the extracted regional signals. A system accuracy of 93.0% was obtained for the automated symmetry classification of transfers. The techniques proposed in this paper can increase the precision and efficiency in clinical SiSt assessments. Their unobtrusive nature makes them particularly suitable for integration into a continuous monitoring system such as those required within the smart home environment.

A Pressure Sensitive Home Environment

Homes could be equipped with unobtrusive pressure sensors to monitor older adults. This paper deals with the processing, analysis and communication of pressure sensor outputs that would enable such monitoring. An example is shown of an adult of 63 years who slept over top of a pressure sensor array. Her nocturnal respiratory rate was monitored via the pressure sensor array. Additionally, her bed time, rise time, and out-of-bed times were accurately recorded using the methods proposed herein. This information was presented through a secure Web interface, which would allow a caregiver simple and intuitive access to client data

Context-aware smart home monitoring through pressure measurement sequences

This paper presents the architecture, sensing and results of a context-aware smart home monitoring system based on pressure measurement sequences. It focuses on the analysis of transfers performed by the occupant in the bedroom and bathroom to assess if their behavior is within a normal range of motion. Pressure sensors are placed under the bed mattress and embedded in the grab bars of a toilet commode to collect data sequences in the form of different modalities. From these sequences, relevant clinical features are extracted and fused with both past and expected results to output a warning level. The data fusion outputs are then fed into a simulated context-aware classifier before a final decision regarding the occupant is made. The pressure sensing system can be modified depending on a particular occupants needs, and can also fit well within a larger and more complex smart monitoring framework.

Breathing Signal Fusion in Pressure Sensor Arrays

Pressure sensors can be used to unobtrusively obtain breathing signals from a person in bed. Obtaining a single representation of the breathing signal from an array of such sensors requires data-level fusion. We propose a decision directed adaptive linear estimator to perform this fusion online. The proposed method was compared with three other online fusion methods and two offline methods using one hundred data records collected from five healthy participants. The decision directed adaptive linear estimator had signal to noise ratios comparable to the offline correlation method that it was adapted from and better mutual information results. In the presence of movement noise and for low amplitude signals, the proposed method also provides good fusion performance.

Design of a capacitive ECG sensor for unobtrusive heart rate measurements

The increased prevalence of cardiovascular disease among the aging population has prompted greater interest in the field of smart home monitoring and unobtrusive cardiac measurements. This paper introduces the design of a capacitive electrocardiogram (ECG) sensor that measures heart rate with no conscious effort from the user. The sensor consists of two active electrodes and an analog processing circuit that is low cost and customizable to the surfaces of common household objects. Prototype testing was performed in a home laboratory by embedding the sensor into a couch, walker, office and dining chairs. The sensor produced highly accurate heart rate measurements (<; 2.3% error) via either direct skin contact or through one and two layers of clothing. The sensor requires no gel dielectric and no grounding electrode, making it particularly suited to the “zero-effort” nature of an autonomous smart home environment. Motion artifacts caused by deviations in body contact with the electrodes were identified as the largest source of unreliability in continuous ECG measurements and will be a primary focus in the next phase of this project.

Force Estimation with a Non-Uniform Pressure Sensor Array

Embedding pressure sensors into household fixtures enables unobtrusive occupant health and safety monitoring at home. To monitor bathroom grab bar use, the force applied to a grab bar is desired from the output of three embedded pressure sensors. We examine the measurement of applied force in a non-uniform pressure sensor array, where forces are distributed with spatial nonlinearity to the pressure sensors. Two methods that ignore the spatial nonlinearities are compared to two methods that incorporate them. These include a polynomial response curve, a theoretical model, a lookup table, and an artificial neural network. When many calibration points can be taken and location estimates are accurate, the location-based lookup table presented the lowest error. However, when calibration time is limited, the theoretical model performs best, while an artificial neural network is preferred when location inputs are inaccurate.

Contact Location Estimation from a Nonlinear Array of Pressure Sensors

In physical medicine and rehabilitation, it is important to be able to collect information regarding a patients behavior and range of mobility throughout their daily activities. Grab bars are used widely in the homes of individuals with mobility impairments so their usage while performing physical tasks can provide valuable information as to the individuals physical status. This paper explores the extraction of location information for forces applied to a grab bar embedded with a nonlinear pressure sensor array of low spatial resolution. It first describes the instrumentation of the grab bar and the calibration procedure. It then investigates three methods of estimating the contact location; a simple centroid, a percentage-based lookup table and an artificial neural network. Results of the three methods are reported based on data collected from different forces and contact locations applied along the bar. The artificial neural network proves to be the most successful method of estimating the points of contact, by most accurately modeling the nonlinearities in the system.

Sit-to-stand timing measurements using pressure sensitive technology

Measuring and analyzing the sit-to-stand movement performed by an individual when rising from their bed can be used to document health and mobility. This paper investigates measuring sit-to-stand timing from young healthy subjects using non-invasive pressure sensitive array technology. First, it describes the pressure sensitive array, the experiment setup and data collection process. Next, three methods for measuring sit-to-stand timing are examined. These include a method based on the combination of bed and foot pressure, an image-based method and a pressure signal-based method. Challenges, limitations and results are shown for each using data collected from 10 young healthy subjects. The pressure signal-based method measures the sit-to-stand time more effectively when comparing to video data. The further development of this method is then presented; this includes analyzing the pressure signal to detect phases that make up the sit-to-stand movement and a sample result for one young healthy subject is presented.