Instance based human physical activity(hpa) recognition using shimmer2 wearable sensor data sets

Doreswamy,; Yogesh, K M

doi:10.1109/icacci.2017.8125971

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…After we have selected the mHealth data sets for Activity Recognition (AR) we can setup new Human Activity Recognition (HAR) system which can divide 70% of train data and 30% of test data for extracting features. To get mHealth data then divide data into 5/sec segment then generate time domain based feature that we are based 256 readings of acceleration sensor data readings, each reading have x, y and co-ordinates equivalent to the three-dimension values [9]. Table 1 shows the dispersion of instances of the different daily physical activities in the feature sets.…”

Section: Methodsmentioning

confidence: 99%

Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

2019

IJRTE

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 Abstract: Human physical activity recognition is one of the important attributes for well being of human health and activity recogni-tion field. A physical signal with respect to time based directed toward recognizes human activity events is projected, in the view of this work usage wearable accelerometer sens-ing devices were implanted taking place the human body lo-cation on the upper body Chest Sensor (CS), Left Ankle Sen-sor (LAS) and Right Lower Arms Sensor (RLAS). Accelerome-ter feature extracted based acceleration signals with respect to time, physical appearance of the accelerometer x, y, and z dimension values reported/recorded using shimmer2 weara-ble sensor device is recommended at the categorization of the 10 different users was performed 12 different types human activities, including vigorous and moderate activities. User ages between 24 to 29 years and human body weight (HBW) are 53 to 83 Kg=m2. Results were on view a large validity per-formance precision and recall were getting 95 for each human activities. The whole classifiers accuracy results for all combi-nation of the feature set of all sensors is 98%. The considered work could be used to observe the human body motion of different body location of users and to perform data analysis and classification.

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Section: Methodsmentioning

confidence: 99%

Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

2019

IJRTE

View full text Add to dashboard Cite

show abstract

Instance based human physical activity(hpa) recognition using shimmer2 wearable sensor data sets

Cited by 1 publication

References 12 publications

Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

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