TY - GEN
T1 - Design of activity recognition systems with wearable sensors
AU - Khokhlov, Igor
AU - Reznik, Leon
AU - Cappos, Justin
AU - Bhaskar, Rohit
N1 - Funding Information:
ACKNOWLEDGMENT This material is partially based upon work supported by the National Science Foundation under awards # ACI-1547301 and # ACI-1547290.
PY - 2018/4/11
Y1 - 2018/4/11
N2 - Wearable sensors are widely utilized in human activity monitoring and recognition systems. Not only do these sensors come in different form factors but the software that comes bundled with them also varies from device to device and is constantly evolving. Also, multiple types of sensors on these devices are used to recognize human activity. Owing to the flexible form factor, these devices can also be mounted on a plethora of different positions on the human body. With all the aforementioned variables, it becomes imperative that the quality of data provided by wearable sensors needs to be evaluated. This paper describes an empirical study resulting in evaluating the accuracy of human activity recognition by wearable sensors based on the type of sensor, the physical mounting position of the sensor on the human body, their type of activity being monitored and the type of device being used. The paper further delves into assessing the results of this study. It provides guidelines for designing better wearable sensor systems for human activity recognition.
AB - Wearable sensors are widely utilized in human activity monitoring and recognition systems. Not only do these sensors come in different form factors but the software that comes bundled with them also varies from device to device and is constantly evolving. Also, multiple types of sensors on these devices are used to recognize human activity. Owing to the flexible form factor, these devices can also be mounted on a plethora of different positions on the human body. With all the aforementioned variables, it becomes imperative that the quality of data provided by wearable sensors needs to be evaluated. This paper describes an empirical study resulting in evaluating the accuracy of human activity recognition by wearable sensors based on the type of sensor, the physical mounting position of the sensor on the human body, their type of activity being monitored and the type of device being used. The paper further delves into assessing the results of this study. It provides guidelines for designing better wearable sensor systems for human activity recognition.
KW - activity recognition
KW - machine learning
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85050136751&partnerID=8YFLogxK
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U2 - 10.1109/SAS.2018.8336752
DO - 10.1109/SAS.2018.8336752
M3 - Conference contribution
AN - SCOPUS:85050136751
T3 - 2018 IEEE Sensors Applications Symposium, SAS 2018 - Proceedings
SP - 1
EP - 6
BT - 2018 IEEE Sensors Applications Symposium, SAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Sensors Applications Symposium, SAS 2018
Y2 - 12 March 2018 through 14 March 2018
ER -