ADL recognition based on the combination of RFID and accelerometer sensing

The manual assessment of activities of daily living (ADLs) is a fundamental problem in elderly care. The use of miniature sensors placed in the environment or worn by a person has great potential in effective and unobtrusive long term monitoring and recognition of ADLs. This paper presents an effective and unobtrusive activity recognition system based on the combination of the data from two different types of sensors: RFID tag readers and accelerometers. We evaluate our algorithms on non-scripted datasets of 10 housekeeping activities performed by 12 subjects. The experimental results show that recognition accuracy can be significantly improved by fusing the two different types of sensors. We analyze different acceleration features and algorithms, and based on tag detections we suggest the best tagspsila placements and the key objects to be tagged for each activity.

[1]  Irfan A. Essa,et al.  Discovering Characteristic Actions from On-Body Sensor Data , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[2]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[3]  Matthai Philipose,et al.  In-Home Assessment of the Activities of Daily Living of the Elderly , 2005 .

[4]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[5]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.

[6]  Jesse Hoey,et al.  Assisting persons with dementia during handwashing using a partially observable Markov decision process. , 2007, ICVS 2007.

[7]  Kristof Van Laerhoven,et al.  Memorizing What You Did Last Week: Towards Detailed Actigraphy With A Wearable Sensor , 2007, 27th International Conference on Distributed Computing Systems Workshops (ICDCSW'07).

[8]  S. Shankar Sastry,et al.  Physical Activity Monitoring for Assisted Living at Home , 2007, BSN.

[9]  Howard D. Wactlar,et al.  Dining activity analysis using a hidden Markov model , 2004, ICPR 2004.

[10]  Matthai Philipose,et al.  Hands-on RFID: wireless wearables for detecting use of objects , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[11]  Paul Lukowicz,et al.  Using multiple sensors for mobile sign language recognition , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[12]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[13]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[14]  Paul Lukowicz,et al.  Gesture spotting using wrist worn microphone and 3-axis accelerometer , 2005, sOc-EUSAI '05.

[15]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[16]  Kristof Van Laerhoven,et al.  Recording Housekeeping Activities with Situated Tags and Wrist-Worn Sensors: Experiment Setup and Issues Encountered , 2007 .

[17]  Sydney Katz.  Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living , 1983, Journal of the American Geriatrics Society.

[18]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[19]  Matthai Philipose,et al.  Common Sense Based Joint Training of Human Activity Recognizers , 2007, IJCAI.

[20]  Gerhard Tröster,et al.  Probabilistic parsing of dietary activity events , 2007, BSN.