IEEE 802 . 15 . 6-based Multi-Accelerometer WBAN System for Monitoring Parkinson ' s Disease

In this paper we present a detailed example of a wireless body area network (WBAN) scenario utilizing the recent IEEE802.15.6 standard as applied to a multiaccelerometer system for monitoring Parkinson’s disease and fall detection. Ultra wideband physical layer and standard security protocols are applied to meet application requirements for data rate and security.

[1]  Jeffrey M. Hausdorff,et al.  Toward Automated, At-Home Assessment of Mobility Among Patients With Parkinson Disease, Using a Body-Worn Accelerometer , 2011, Neurorehabilitation and neural repair.

[2]  Kyung Sup Kwak,et al.  An overview of IEEE 802.15.6 standard , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[3]  Kamiar Aminian,et al.  Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Zhihua Wang,et al.  Low power, non invasive UWB systems for WBAN and biomedical applications , 2010, 2010 International Conference on Information and Communication Technology Convergence (ICTC).

[5]  Houeto Jean-Luc.  [Parkinson's disease]. , 2022, La Revue du praticien.

[6]  Patty S. Freedson,et al.  A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations , 2011, European Journal of Applied Physiology.

[7]  Harri Viittala,et al.  Medical applications adapting ultra wideband: a system study , 2010, Int. J. Ultra Wideband Commun. Syst..

[8]  Shyamal Patel,et al.  Mercury: a wearable sensor network platform for high-fidelity motion analysis , 2009, SenSys '09.

[9]  Tim Lüth,et al.  A measurement device for motion analysis of patients with Parkinson's disease using sensor based smart clothes , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[10]  Matti Hämäläinen,et al.  IEEE 802.15.4a UWB receivers in medical applications , 2011, Int. J. Ultra Wideband Commun. Syst..

[11]  John R. Long,et al.  Principles and Limitations of Ultra-Wideband FM Communications Systems , 2005, EURASIP J. Adv. Signal Process..

[12]  Stan C A M Gielen,et al.  Ambulatory motor assessment in Parkinson's disease , 2006, Movement disorders : official journal of the Movement Disorder Society.

[13]  Pierre J. M. Cluitmans,et al.  Detection of Subtle Nocturnal Motor Activity From 3-D Accelerometry Recordings in Epilepsy Patients , 2007, IEEE Transactions on Biomedical Engineering.

[14]  Maarit Kangas,et al.  Comparison of low-complexity fall detection algorithms for body attached accelerometers. , 2008, Gait & posture.

[15]  T. Dimitriou,et al.  Security issues in biomedical wireless sensor networks , 2008, 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies.

[16]  Ryuji Kohno,et al.  UWB systems for body area networks in IEEE 802.15.6 , 2011, 2011 IEEE International Conference on Ultra-Wideband (ICUWB).

[17]  Paolo Bonato,et al.  Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures , 2012, IEEE Transactions on Biomedical Engineering.

[18]  Pardeep Kumar,et al.  Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey , 2011, Sensors.

[19]  Ryuji Kohno,et al.  Ultra low power UWB transceiver design for body area networks , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[20]  Bart Vanrumste,et al.  Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: A first study , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  J. J. van Hilten,et al.  Accelerometric assessment of levodopa‐induced dyskinesias in Parkinson's disease , 2001, Movement disorders : official journal of the Movement Disorder Society.

[22]  C. Pollak,et al.  The role of actigraphy in the study of sleep and circadian rhythms. , 2003, Sleep.

[23]  Luca Palmerini,et al.  Feature Selection for Accelerometer-Based Posture Analysis in Parkinson's Disease , 2011, IEEE Transactions on Information Technology in Biomedicine.