Biosensors integrated into the vehicle controller area network are used for detecting symptoms such as anxiety, pain, and fatigue that may affect driving safety. The proposed system provides a flexible option for implementation in a diverse range of mass-produced automotive accessories without affecting the driver's movement

Chan, C.

Fong, A.C.M.

Fong, B.

Situ, L.

English

A. C. M. Fong

C. Chan

L. Situ

B. Fong

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Wireless biosensing network for drivers' health monitoring

Enlighten

      Fong, A.C.M., Chan, C., Situ, L., and Fong, B. (2016) Wireless biosensing network for drivers' health monitoring. In: 2016 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, USA, 07-11 Jan 2016, pp. 247-248. ISBN 9781467383639.    There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it.      http://eprints.gla.ac.uk/123209/            Deposited on: 24 August 2016                       Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk Abstract—Biosensors integrated into the vehicle controller area network are used for detecting symptoms such as anxiety, pain, and fatigue that may affect driving safety.  The proposed system provides a flexible option for implementation in a diverse range of mass-produced automotive accessories without affecting the driver’s movement.   I. INTRODUCTION Onboard diagnostics (OBD) systems become an integral part of modern motor vehicles for continuous assessment of vehicle system health [1].  Additional features for monitoring various health parameters of the driver can be accomplished through connection of appropriate biosensors such as that deployed in a smart home environment [2].  Any anomaly detected will trigger an alarm and emergency response can be initiated through a telemedicine link [3].  The implementation is particularly suited for automotive consumer electronics marketing in response to the mandatory inclusion of eCall system in the EU by October 2017 [4] that does not entail retrofitting of any electronics components. This paper describes an implementation approach that utilizes the integrated controller area network (CAN bus) of a motor vehicle for interconnection of different sensors to support both continuous and continual health monitoring of various health signs of a driver.  Its primary function is to trigger an alert in the event of detecting any abnormal readings.   These are therefore more suitable for consumer-grade health monitoring due to their minimal on-board circuitry. II. CONTROLLER AREA NETWORKThe sensing network consists of different non-invasive sensors that require each sensor to operate independently while collecting measurements and forward them to the OBD for processing. Continual health monitoring of parameters such as blood glucose and pressure do not require a continuous power source that can run on harvested power for communication.  On receiving data from the biosensors; the system will commence anomaly event detection.  The biosensor that picks up abnormal reading will be assigned with a higher transmission priority and automatically sends an alarm to the response center [5].  One of the main functions of the OBD is to control the state of the software-selectable CAN interface and detects anomaly based on data received from the biosensing network with specifications summarized in Table I. This work is partially supported by the Innovation and Technology Commission R&D Cash Rebate Scheme CRP/002/15. TABLE I SENSING NETWORK PARAMETERS  Network Parameter SpecificationBiosensors  Transmission Power 2 dBm Driver Health Packet Size 512 Bytes (Wearable) Packet Inter-arrival Time > 10 ms Nominal BER 10-4Controller Area  Transmission Power 10 dBm Network (CAN) Packet Size 8 192 Bytes Data Rate 11 Mb/sNetwork Bandwidth 4 MHzCenter frequency 2.4 GHz III. CONSUMER HEALTH AUTOMOTIVE ACCESSORIESA. Biosensors placement The most economical means of installation without entailing any physical alteration to the motor vehicle is to install wireless sensors on various automotive accessories depending on the health signs being monitored, these include seat pads, steering wheel covers and wearables such as smart cloths and watch [6]. Our preliminary study aims at providing general health assessment for back pain and fatigue. B. Implementation The initial deployment studies back pain caused by prolonged driving that involves the use of three sensors attached to the upper trunk. Two of them were attached bilaterally to the acromions and the last one was attached to the spinous process of the seventh cervical spine (C7).  A rigid lumbar cluster was positioned at L1 spinous process to define the orientation of lower trunk.  In addition, sensors are attached from proximal to distal, down from knee, leg, ankle and to foot.  The knee sensor was placed over the lateral epicondyle of femur. The leg sensor was placed at about distal on-third of the most lateral aspect of leg. The ankle sensor was placed at lateral malleolus. Finally, the foot sensor was placed at the second metatarsal head.  Driver’s leg length was measured from ASIS to medial malleolus for the calculation of the hip joint center [7].  The sensors continually track the driver’s movement and the collected data is forwarded to the OBD for analysis. Fig. 1 Posterior view of driver with biosensors and EMG electrode placements  Wireless Biosensing Network for Drivers’ Health Monitoring C. Performance evaluation Fig. 3 Selected parameters for electromyographic analysis The muscle activities of bilateral erector spinae and multifidus were monitored by surface electromyography (EMG). Electrodes for erector spinae were attached bilaterally to the greatest convexity of the muscle bulk by palpation [8]. Skin impedance for each pair of electrodes was below 50 kΩ. The peak and mean values of EMG of erector spinae and multifidus were analyzed, EMG signals of each muscle were recorded for 5 seconds.  D. Network optimization The CAN bus is shared by different subnetworks within the motor vehicle including infotainment, vehicle system health diagnosis along with the driver health monitoring system. Since the biosensor network consists of sensor nodes that sense changes in the vehicle cabin which enables real-time monitoring of Point of Care (PoC) as shown in Fig. 4.  Fig. 4 Network architecture A scalable network is optimized using a prognostics approach [9], [10] whose transmission characteristics are shown in Fig. 5 where Eb/No is evaluated for the sensing network. Fig. 5 Network performance evaluation IV. CONCLUSIONSWireless sensors are connected via a motor vehicle onboard diagnosis system that monitors various health signs using a prognostics methodology.  Such implementation is particularly suited for the mass-produced consumer healthcare industry due to minimal additional hardware requirements and ease of installation without affecting other parts of the vehicle. REFERENCES [1] B. Fong, et. Al., "A Prognostics Framework for Reliability Optimization of Mass-Produced Vehicle Onboard Diagnostics System," IEEE Global Conference on Consumer Electronics, Osaka, Oct. 2015. [2] C. Y. Leong, A. R. Ramli and T. Perumal, “A rule-based framework for heterogeneous subsystems management in smart home environment”. IEEE Transactions on Consumer Electronics, vol. 55, no. 3, pp. 1208-1213. 2009 [3]  N. Ansari, B. Fong and YT Zhang, “Wireless technology advances and challenges for telemedicine”, IEEE Communications Magazine, vol. 44, no. 4, pp. 39–40, 2006. [4] eCall: emergency call system in road accidents, Council of the European Union PE- ?CO S 77/14, 02 March 2015. [5] K. C. Lee and H. H. Lee, "Network-based fire-detection system via controller area network for smart home automation.", IEEE Transactions on Consumer Electronics, vol. 50, no. 4, pp. 1093-1100. 2004. [6] B. Fong, A. C. M. Fong and C. K. Li, “Telemedicine Technologies: Information Technologies in Medicine and Telehealth”, Wiley 2011. [7] R. B. Davis, et. Al., “A gait analysis data collection and reduction technique”. Human Movement Science, vol. 10, pp. 575-587, 1991. [8] A. R. Humphrey, et. Al., “The value of electromyography of the lumbar paraspinal muscles in discriminating between chronic-low-back-pain sufferers and normal participants.” European Spine Journal, .vol. 14, pp. 175–184, 2004. [9] B. Fong, et. Al., “On the scalability of fixed broadband wireless access network deployment.”, IEEE Communications Magazine, vol. 42, no. 9, pp. S12-S18, 2004. [10] B. Fong, N. Ansari and A. C. M. Fong, “Prognostics and health management for wireless telemedicine networks.” IEEE Wireless Communications, vol. 19, no. 5, pp. 83-89, 2012 

Wireless Biosensing Network for Drivers' Health Monitoring

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Wireless Biosensing Network for Drivers' Health Monitoring

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