Anomaly detection in smart houses: Monitoring elderly daily behavior for fall detecting

Smart Houses and Internet of Things (IoT) are two present tendencies in our days. Due to these technologies, the existent types of equipment in a smart house (sensors, thermostats, and video cams) allow us to analyze and collect data from a person's daily activities and use it in the field of anomaly detection. Therefore, noninvasive monitoring techniques can be applied to people's residences. When focusing on the elderly population, this type of approach can be used to detect and report a fall, decreasing the costs of monitoring these individuals. This paper uses images from a Microsoft Kinect cam, accelerometers' data, digital image processing and computer vision techniques to make a comparative study between different supervised classifiers and statistic approaches when they are being used in the fall detection problem. The results show that some of the tested classifiers are efficient in this task, reaching an accuracy of 96.67% and 98.79%.

[1]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[2]  Richard Harper,et al.  Inside the Smart Home , 2003, Springer London.

[3]  Enamul Hoque,et al.  Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities , 2015, 2015 International Conference on Distributed Computing in Sensor Systems.

[4]  S. Mukhopadhyay,et al.  Activity and Anomaly Detection in Smart Home: A Survey , 2016 .

[5]  Albert Ali Salah,et al.  Seventh International Workshop on Human Behavior Understanding (HBU 2016) , 2016, ACM Multimedia.

[6]  Vikramaditya R. Jakkula,et al.  Anomaly Detection Using Temporal Data Mining in a Smart Home Environment , 2008, Methods of Information in Medicine.

[7]  Bogdan Kwolek,et al.  Human fall detection on embedded platform using depth maps and wireless accelerometer , 2014, Comput. Methods Programs Biomed..

[8]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[9]  Jeffrey M. Hausdorff,et al.  Risk factors for falls among older adults: a review of the literature. , 2013, Maturitas.

[10]  Claudio Bettini,et al.  From lab to life: Fine-grained behavior monitoring in the elderly's home , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[11]  Paola Pierleoni,et al.  A High Reliability Wearable Device for Elderly Fall Detection , 2015, IEEE Sensors Journal.

[12]  Qian Zhu,et al.  IOT Gateway: BridgingWireless Sensor Networks into Internet of Things , 2010, 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.

[13]  Sankar K. Pal,et al.  Multilayer perceptron, fuzzy sets, and classification , 1992, IEEE Trans. Neural Networks.

[14]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Bulusu Lakshmana Deekshatulu,et al.  Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm , 2015, ArXiv.

[17]  Jihoon Hong,et al.  Strategic management of next-generation connected life: Focusing on smart key and car–home connectivity , 2016 .

[18]  Chung-Horng Lung,et al.  Smart Home: Integrating Internet of Things with Web Services and Cloud Computing , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[19]  Patrick Th. Eugster,et al.  Detecting Abnormalities in IoT Program Executions through Control-Flow-Based Features: Poster Abstract , 2017, IoTDI.

[20]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[21]  J. Kofman,et al.  Review of fall risk assessment in geriatric populations using inertial sensors , 2013, Journal of NeuroEngineering and Rehabilitation.