Purpose: To obtain insight into what kind of monitoring technologies exist to monitor activity in-home, what the characteristics and aims of applying these technologies are, what kind of research has been conducted on their effects and what kind of outcomes are reported. Methods: A systematic document search was conducted within the scientific databases Pubmed, Embase, Cochrane, PsycINFO and Cinahl, complemented by Google Scholar. Documents were included in this review if they reported on monitoring technologies that detect activities of daily living (ADL) or significant events, e.g. falls, of elderly people in-home, with the aim of prolonging independent living. Results: Five main types of monitoring technologies were identified: PIR motion sensors, body-worn sensors, pressure sensors, video monitoring and sound recognition. In addition, multicomponent technologies and smart home technologies were identified. Research into the use of monitoring technologies is widespread, but in its infancy, cons...

Literature review on monitoring technologies and their outcomes in independently living elderly people.

Differentiating dry and wet cough is an important factor in respiratory disease. The main objective of this paper is to analyze cough sounds and extract features that can be used in differentiation of dry and wet cough sounds. This paper proposes two features to achieve this goal. The first feature is the number of peaks of the energy envelope of the cough signal. The second feature is the power ratio of two frequency bands of the second phase of the cough signal. A set of eight highly dry and eight highly wet cough sound recordings were used. Using these two features, a clear separation was observed among the dry and wet cough sound recordings.

https://curve.carleton.ca/system/files/etd/91d9768a-fdfc-4145-a9a4-a2255cdee360/etd_pdf/0ab37fbde60f27301616e43085ba99fb/chatrzarrin-featureextractionforthedifferentiationofdry.pdf

Feature extraction for the differentiation of dry and wet cough sounds

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

Ambient Sensors for Elderly Care and Independent Living: A Survey.

There has been tremendous increase in both the different types of Mobile Health (mHealth) applications and the number of applications being created for both the clinical and consumer healthcare space. The rapid proliferation of mHealth applications is creating confusion in the domain among both consumers and healthcare professionals due to uncertainty about reliability, security, regulation, and integration concerns. New applications are being developed faster than researchers, patients, and healthcare professionals can grasp the multiplicity of the mHealth applications and the various ways they can be used. This paper proposes a taxonomy of existing and emerging mHealth applications to help all users understand this domain.

mHealth taxonomy: a literature survey of mobile health applications

This paper demonstrates the use of a bed-based optical pressure sensor array to unobtrusively recognize sitting and lying postures as well as lie-to-sit postural transitions. Young healthy, older healthy, older post-stroke, and older post-hip-fracture participants performed a bed entry and exit routine. Data was collected using a pressure sensor array and video cameras. Lying and sitting postures and transitions were analyzed by our system and compared to video analysis from two medical students. For posture identification, eight pressure signal features and three classification techniques were compared. For transition detection, a movement detection algorithm was implemented and combined with the posture identification system. Postural detection accuracy of 100% was achievable using a combination of pressure features. Postural transition detection held a very low miss rate. Differences in measurement of transition duration between our system and video analysis were statistically insignificant.

Lying and sitting posture recognition and transition detection using a pressure sensor array

Unobtrusive pressure sensors can be used for biological monitoring and long-term health assessment in smart homes. The challenge in detecting events from smart home data is that people have different mattresses, unlike in hospitals where bedding is standardized. This paper proposes to model central apneas using an under-mattress pressure sensor as a measuring instrument. The model uses three parameters, namely, a relative threshold and two time lengths, applied to a moving variance signal. The use of a relative threshold allows apneas to be detected under a variety of different conditions and improves results compared to hard-coded thresholds. The algorithm developed herein was applied to simulated apneas collected from pressure sensors placed under nine different mattresses. The parameters determined from the training set were applied to the test set and produced classification results of 0.78 positive predictive value (PPV) if the bed occupant's position is known and 0.75 PPV if the position is unknown. The use of the relative threshold approach overcomes the variability in mattress types found in smart homes.

Relative Thresholding With Under-Mattress Pressure Sensors to Detect Central Apnea

The use of unobtrusive sensors for physiological monitoring is growing in popularity. With advantages such as their noncontact nature plus limited physical and cognitive load on the user, unobtrusive sensors will likely increase acceptance and usefulness in biomedical monitoring. Biomedical applications for unobtrusive pressure sensors include the analysis of bed transfer sequences and the extraction of breathing rate during long-term trend analysis and health monitoring. This paper proposes an algorithm to measure torso movement using an unobtrusive pressure-sensor array. This work makes use of the information captured by measuring the respiratory-signal delay between sensors in the array. The novel contributions are using cross-correlation to realign polarity-adjusted signals from a pressure-sensor array and to create a map of delays across the torso. Not only is the advantage in adjusting the polarity of inverted signal clearly demonstrated, but in many cases, the realignment according to the delay increases the signal amplitude and further improves the signal-to-noise ratio in many cases. The map of delays between the pressure sensors' signals reveals important information about movement timing in the torso while breathing.

Measurement of Torso Movement With Delay Mapping Using an Unobtrusive Pressure-Sensor Array

Previous research using pressure sensors to monitor physiological signs, such as breathing and heart rate, has often been in controlled laboratory or institutional settings. We are interested in validating the use of such sensors in diverse home environments, where unobtrusiveness is achieved by embedding pressure sensors inside or below furniture.We have quantitatively examined outputs from sensors placed below a variety of mattress types and compared the response to outputs from sensors placed on top of the mattress. We show that embedded pressure sensors are valid for use in home environments and could be reliably used to collect physiological data. However, resultant low signal levels may require more sophisticated signal extraction algorithms.

Validation of pressure sensors for physiological monitoring in home environments

Respiratory effort signals may be measured using pressure sensors placed below a mattress. When few of the sensors include the respiratory effort signal or the signal power is low, output respiratory effort signal quality is low. This paper examines current signal combining methods used in pressure sensor array breathing estimation and proposes a maximal ratio combining method to produce the best estimate in these conditions. Data was collected from two participants and the effects of low signal power and low signal availability were simulated by corrupting the recordings. The results show that the proposed method offers 2.5 dB higher signal to noise ratio in the presence of substantial noise and good correlation (> 0.92) to gold standard respiration bands.

Maximal ratio combining for respiratory effort extraction from 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.

Megan Holtzman

Papers

Relative Thresholding With Under-Mattress Pressure Sensors to Detect Central Apnea

Measurement of Torso Movement With Delay Mapping Using an Unobtrusive Pressure-Sensor Array

Validation of pressure sensors for physiological monitoring in home environments

Maximal ratio combining for respiratory effort extraction from pressure sensor arrays

Breathing Signal Fusion in Pressure Sensor Arrays