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An Intelligent Integrated Healthcare Platform for Wellbeing and Independent Living

By Subhas Mukhopadhyay and Nagender Suryadevara

Elderly persons generally prefer to live in their own homes. When they are able to do so, societal and economic benefits can follow. Living alone has high risks however. Our project aims to monitor the wellness of elderly persons living alone by unobtrusively monitoring their daily activities.

Our current research is directed towards the development of an Intelligent Integrated Healthcare Platform for Wellbeing and Independent Living. A significant progress has been made and a few of these initial results are listed [1-10]. A technology-assisted intelligent integrated healthcare platform (using a combination of smart sensors, wireless sensor network, computer programme with adaptive learning ability) is under development with the ability to predict, diagnose and monitor the wellness of the inhabitant, especially elderly, in real time. The development will lead to the creation of a novel technology which could integrate, analyse and interpret the collected data and, through a robust decision making process, determine the wellness level of the person, thus allowing him/her to lead a safe, sound, secure and independent lifestyle. The wellness level can be used to summon help when needed. Also it can be used to help recognize unsafe conditions in the home.

The healthcare platform will consist of a few modules: (1) Appliances Tracking Unit, (2) Human Emotion Recognition unit, (3) Physiological Parameters Monitoring Unit, (4) Human Posture and Position Detection Unit, (5) Power Management Unit, (6) Automatic Electronic Medicine Dispenser, (7) Robust Supervisory control unit and (8) Safety Box Unit. The modules can be used as a stand-alone device or all the modules can be combined to work as a complete system.

A few modules (modules# 1 to #6) have already been developed and are currently in trial conditions for collecting real-life data at elderly homes. Figure 1 shows a few wireless sensing units developed for monitoring different appliances which are necessary for daily life of the elderly. The major task of our work is to recognize the essential activities of daily living behaviour of the elderly through sensor fusion by using minimal sensors at elderly home. For this, Wireless Sensor Network (WSN) consisting of different types of sensors like Electrical, Force, Contact sensors with Zigbee module sensing units are installed at the home of the elderly person. The system is developed by following the principles of the IEEE standard 802.15.4 of ZigBee. Communication is established and managed by the functional set of the modem configuration with appropriate values for Network, security, serial and I/O interfacing.

We have developed a mechanism for estimating the well-being condition of elderly based on usage of household appliances connected through various sensing units. We defined two new wellness functions to determine the status of the elderly on performing essential daily activities.

Figure 1: Wireless sensing units monitoring household appliances.
Figure 1: Wireless sensing units monitoring household appliances.

Click to enlarge

To assist the home monitoring system, we have also developed an emotion recognition system based on information provided by the physiological signals. These signals are obtained from a skin temperature sensor, a heart rate sensor, and a skin conductance sensor. The four basic emotions observed in this project are happy (excited), sad (unhappy), angry (distressed) and neutral (relaxed). The platform as is shown in Figure 2 is used for collecting the physiological signals of the person under care. There is no need of continuous measurement, it is only used when the elderly resident would like it to do so. This system will supplement the daily activity behavior recognition to help assess the wellness of the elderly resident.

Figure 2: Sensing unit for human emotion recognition
Figure 2: Sensing unit for human emotion recognition

For Further Reading

1. N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.

2. N. K. Suryadevara, A. Gaddam, R. K. Rayudu and S. C. Mukhopadhyay, “Wireless Sensors Network based safe Home to care Elderly People: Behaviour Detection”, Sens. Actuators A: Phys. (2012), doi:10.1016/j.sna.2012.03.020, Volume 186, 2012, pp. 277 – 283.

3. A. Gaddam, S. C. Mukhopadhyay, and G. Sen Gupta, “Elder Care Based on Cognitive Sensor Network”, IEEE Sensors Journal, Vol. 11, No. 3, 2011, pp. 574-581.

4. N. K. Suryadevara, S. C. Mukhopadhyay, R. K. Rayudu and Y. M. Huang, “Sensor Data Fusion to determine Wellness of an Elderly in Intelligent Home Monitoring Environment,” Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 947-952.

5. T. Quazi, S. C. Mukhopadhyay, N. K. Suryadevara and Y. M. Huang, “Towards the Smart Sensors Based Human Emotion Recognition,” Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 2365-2370.

6. H. Al-Abri, S. C. Mukhopadhyay, G. A. Punchihewa, N. K Suryadevara and Y. M. Huang, “Comparison of applying Sleep Mode function to the Smart Wireless Environmental Sensing Stations for Extending the Life time,” Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 2634-2639.

7. N. K. Suryadevara, S. C. Mukhopadhyay and R. K. Rayudu, “Applying SARIMA Time Series to Forecast Sleeping Activity for Wellness Model of Elderly Monitoring in Smart Home,” Proceedings of the 2012 Sixth International Conference on Sensing Technology, ISBN 978-1-4673-2245-4, Kolkata, India, Dec. 18-21, 2012, pp. 444-449.

8. S. P. S. Gill, N. K. Suryadevara and S. C. Mukhopadhyay, “Smart Power Monitoring System Using Wireless Sensor Networks,” Proceedings of the 2012 Sixth International Conference on Sensing Technology, ISBN 978-1-4673-2245-4, Kolkata, India, Dec. 18-21, 2012, pp. 444-449.

9. N. K. Suryadevara, M. T. Quazi and S .C. Mukhopadhyay, “Intelligent Sensing Systems for measuring Wellness Indices of the Daily Activities for the Elderly,” Proceedings of the 2012 Eighth International Conference on Intelligent Environments, Mexico, June 1-3, 2012, pp. 347-350.

10. A. Gaddam, S. C. Mukhopadhyay, G. Sen Gupta, “Trial & Experimentation of a Smart Home Monitoring System for Elderly,” Proceedings of IEEE I2MTC 2011, doi: 10.1109/IMTC.2011.5944230, 10-12 May 2011, pp. 1 – 6.

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March 2013 Contributors

Nitish V. ThakorNitish V. Thakor is a Professor of Biomedical Engineering at Johns Hopkins University, Baltimore, USA, as well as the Director of the newly formed institute for neurotechnology, SiNAPSE, at the National University of Singapore. Read more

Jerrold L. VitekJerrold L. Vitek is Professor and Head of the Department of Neurology at the University of Minnesota. Dr. Vitek received his medical degree as well as a doctorate of neurophysiology... Read more

Subhas MukhopadhyaySubhas Mukhopadhyay graduated from the Department of Electrical Engineering, Jadavpur University, Calcutta, India with a Gold medal and received the... Read more

Nagender SuryadevaraNagender Suryadevara received his Bachelor of Technology degree in Computer Science and Engineering from Sri Krishnadevaraya University, Anantapur, India in 1996... Read more

Dinesh KumarDinesh Kumar graduated from IIT Madras, and later did his PhD from IIT and AIIMS, Delhi. After a successful stint in starting and running a high-tech venture... Read more

Bill AshBill Ash is Strategic Program Manager for the IEEE Standards Association (IEEE-SA). He received his BSEE from Rutgers University School of the Engineering. Read more

Samuel SciaccaSamuel Sciacca is CEO of SCS Consulting, LLC. He is a registered Professional Engineer and an active Senior Member in IEEE and the IEC in the areas of utility automation... Read more