Machine Learning Models for Human Fall Detection using Millimeter Wave Sensor

Accidental falls are a common threat to the health of older adults, which can reduce their ability to remain independent. Fall detection sensors have become essential lifesaving health monitoring systems for the elderly. We describe a privacy protecting system for stance monitoring of occupants within a room using a millimeter wave (mmWave) sensor. We studied various machine learning models that are best suited to analyze the response from a mmWave system output. After comparing several machine learning algorithms, we found that feedforward neural networks provide the highest test accuracy.

[1]  Xiaoyuan Fan,et al.  Data-Driven Feature Analysis in Control Design for Series-Compensated Transmission Systems , 2019, IEEE Transactions on Power Systems.

[2]  N. Noury,et al.  Evaluation of the autonomic nervous system for fall detection , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Moeness Amin,et al.  Fall Detection Using Deep Learning in Range-Doppler Radars , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[5]  S. Stanko,et al.  Synthetic aperture radar at millimeter wavelength for UAV surveillance applications , 2015, 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[6]  Yimin Zhang,et al.  Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring , 2016, IEEE Signal Processing Magazine.

[7]  E. Skafidas,et al.  Consumer radar: Opportunities and challenges , 2014, 2014 11th European Radar Conference.

[8]  Tao Yang,et al.  Placement selection of millimeter wave FMCW radar for indoor fall detection , 2018, 2018 IEEE MTT-S International Wireless Symposium (IWS).

[9]  Liang Liu,et al.  Automatic fall detection based on Doppler radar motion signature , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[10]  E. Burns,et al.  Deaths from Falls Among Persons Aged ≥65 Years — United States, 2007–2016 , 2018, MMWR. Morbidity and mortality weekly report.

[11]  Sandeep Rao,et al.  The fundamentals of millimeter wave sensors , 2017 .

[12]  Salim Hariri,et al.  Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs , 2019, 2019 IEEE Radar Conference (RadarConf).

[13]  Piotr Duda,et al.  New Splitting Criteria for Decision Trees in Stationary Data Streams , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[16]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[17]  Kaishun Wu,et al.  WiFall: Device-free fall detection by wireless networks , 2017, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  Heng-Yi Su,et al.  Enhanced-Online-Random-Forest Model for Static Voltage Stability Assessment Using Wide Area Measurements , 2018, IEEE Transactions on Power Systems.

[19]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[20]  Dominique Schreurs,et al.  Radar-Based Health Monitoring , 2013, 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO).

[21]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[22]  C. Vandelanotte,et al.  The effect of eHealth-based falls prevention programmes on balance in people aged 65 years and over living in the community: protocol for a systematic review of randomised controlled trials , 2020, BMJ Open.

[23]  Tengyu Ma,et al.  CS229 Lecture notes , 2007 .