Towards detecting and predicting fall events in elderly care using bidirectional electromyographic sensor network

Falling is one of the most serious life-threatening events for the elders, and the ICT-based solution plays a key role in addressing this problem prevalently. In this paper, four principles are proposed as fundamental criteria for designing a sensor network for elder-oriented fall detection and prediction. According to these criteria, a bidirectional electromyographic sensor network model is experimentally constructed, and qualitative analysis is conducted to explain that this solution performs more realistically and rationally.

[1]  Michael J. McGrath,et al.  Falls Prevention in the Home: Challenges for New Technologies , 2011 .

[2]  Luigi Raffo,et al.  Home telemonitoring of vital signs through a TV-based application for elderly patients , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[3]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[4]  Takao Someya,et al.  Printable elastic conductors with a high conductivity for electronic textile applications , 2015, Nature Communications.

[5]  Li-Wei Chan,et al.  CyclopsRing: Enabling Whole-Hand and Context-Aware Interactions Through a Fisheye Ring , 2015, UIST.

[6]  Surapa Thiemjarus,et al.  Automatic Fall Monitoring: A Review , 2014, Sensors.

[7]  Shinichiroh Yokota,et al.  Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data. , 2016, Japan journal of nursing science : JJNS.

[8]  A. Daley,et al.  HOSPITAL STATISTICS , 1955, Lancet.

[9]  Mike Y. Chen,et al.  BackHand: Sensing Hand Gestures via Back of the Hand , 2015, UIST.

[10]  Steve Mann Smart clothing , 1997, Pers. Ubiquitous Comput..

[11]  Hisashi Kobayashi,et al.  Signal strength based indoor geolocation , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[12]  Yang Zhang,et al.  Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition , 2015, UIST.

[13]  Alessio Vecchio,et al.  Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey , 2010 .

[14]  Hassan Ghasemzadeh,et al.  A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities , 2010, IEEE Transactions on Information Technology in Biomedicine.

[15]  Marco Benvenuto,et al.  A novel approach for design and testing digital m-health applications , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.