The New York Times published a front page story regarding how sensor technology is changing the face of home health care. The article discussed how increasingly, many older people who live alone are not truly alone. They are being watched by a flurry of new technologies designed to enable them to live independently and avoid expensive trips to the emergency room or nursing homes.
Crossbow's wireless sensor network technology has been used in a plethora of various research and project deployments on the capabilities of integrating wireless sensor network technology for elder care. Recently, researchers at Kalasalingam University in Nadu, India published their project details in the Jan/Feb Issue of Telemedicine and eHealth on using the IRIS and Imote2 platforms for home health monitoring. The issue they address is real as the increasing demand on public health care due to the aging population has become a major problem in developed countries. With the increasing number of elders relying on home care, better monitoring and analysis systems are crucial for maintaining and improving the quality of life for the elder patients. The concept of health monitoring is advanced by which health parameters are automatically monitored at home without disturbing daily activities. The proposed system is a network that supports various wearable sensors and contains on-board general computing capabilities for individual event detection, alerts, and communications with various medical informatics services. The purpose of their system is to provide extended monitoring for elderly patients under drug therapy after infarction, data collection in some particular cases, and remote consultation for elderly people.
With the advancement in ubiquitous computing techniques, researchers at Kalasalingam University proposed this new system for health monitoring at home to assess the elderly peoples’ health status. The integration of sensing, information, and communication technologies allowed elderly people to be constantly monitored. Moreover, constant monitoring would increase early detection of adverse conditions and diseases in elderly patients, potentially saving more lives. The proposed healthcare system was built upon a global medical information system made up of three main components: (1) Various sensors of different types to monitor physiological signals of elderly patients, their environments, and their activities with sensor nodes kept in the patients’ home; (2) Sensor nodes to transmit the signal from sensors to central servers through a base station; and (3) Central server for management of a knowledge database related to patients and responsible for broadcasting messages and alarms to healthcare professionals and caretakers. The system proposed a technique for monitoring elderly patient status at home and detecting critical situations for the purpose of alerting the doctors as well as the caretakers. It consists of a front end, the patient station, and the server.
The Patient Station, consists of an IRIS (XM2110CA) sensor node, Crossbow's newest 2.4GHz Mote platform for ultra low-power, long-range wireless sensor networking that receives information from the physiological sensors. The 3D camera kept at home was connected with the Imote2 platform (IPR2400CA) to visually monitor the elderly patient in the home environment in addition to the physiological signals. The Imote2 sensor node is aimed at applications involving data-rich computations, where there is a need for both high performance and high bandwidth, which require greater processing capability and low-power operation with a low duty cycle to achieve longer battery life. Sensor nodes are responsible for sensing as well as the first stage of sensory data processing in the data communication. The received signal from the patient station was transmitted to the central server for analysis. Researchers found that the benefits of the IRIS mote included excellent radio frequency range, substantially lower sleep current, and double the program memory of other Mote offerings. By Integrating a high-performance, low-power PXA271 Intel processor and an 802.15.4 radio with a built-in 2.4-GHz antenna, the Imote2 provided a platform for digital imaging applications.
Researchers placed sensor nodes equipped with flex sensors on the chest for ECG, and on the right hand for heart rate and acceleration measurement. The IRIS sensor node placed on the body was responsible for collecting physiological data and movement data from the sensors and transmitting it to the central servers through the aggregation node. The aggregation sensor nodes do not act merely as data collecting and forwarding points. Instead, they make decisions whether the information should be stored for future use, relayed as they are, or modified by applying computation and aggregation with other data.
The server, which is the core processing element, received the patient’s data from the patient station through the base station and stored the data into the patient database, while performing long-term trend analyses and prediction. It dispatches the critical events detected by sensor nodes to healthcare professionals. Moreover, it coordinates and controls the overall functionality of the system. This system can also give the alert to the doctors via a personal digital assistant (PDA) or cell phone. The sensor nodes could be programmed to awaken the node whenever an abnormal signal is detected and transmit the data to the server and then return to sleep mode. When an abnormality was detected in the sensor node, it would automatically turn on and transmit the signal to the central server and simultaneously alert the caretaker near the patient. The server, which is the core processing element, received the data regularly from the sensor nodes for analysis. The analysis was done using Crossbow's MoteView, and operates under TinyOS, which offers a built-in library for data acquisition, processing, analysis, and display.
The central servers were programmed with two algorithms. First was the threshold based algorithm, which attempted to identify the physiological parameter values that were potentially harmful or indicative of immediate danger to the patients. The algorithm detected the upper and lower threshold values from sensors output and alerted medical personnel and caretakers when the patient was not physically capable of requesting help. Second was the inactivity detection algorithm for detecting rapid movement or lack of movement of elder patients from accelerator output. The sensor node was programmed through the central server in such a way that if lack of movement/ action is detected, it awakened the Imote2 device connected to a 3-D camera fixed in that specific room to send the status of the elder patient to a central server. Upon detecting an anomalous event, both algorithms would sound an auditory alarm from the central server to emergency departments and alert the caretakers near the patients.
In this implementation, researchers at Kalasalingam University were able to connect two different systems, that is, the patient record database and the Web portal, through the use of well-defined Web services. Patient information was transmitted over SOAP, a secure and encrypted form of XML. The WSDL (Web Service Definition Language) for these Web services is published to a community of authorized users. This Web-service based approach for inter-system communication gives the system the flexibility to operate with third-party software in the future.The proposed system used a wireless sensor network technology within the residence in order to increase functionality, security, and quality of life.
As the Times put it, 'The future of these technologies, and the terabytes they gather, can involve unprecedented information about the whereabouts and well-being of older people.'
