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Dataset for IoT Assisted Human Posture Recognition
- Citation Author(s):
-
NitinGupta
National Institute of Technology, Hamirpur - Submitted by:
- Ritwik Duggal
- Last updated:
- Thu, 11/12/2020 - 09:34
- DOI:
- 10.21227/gk5q-gw14
- Data Format:
- License:
3944 Views- Categories:
- Keywords:
Abstract
The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. Most of the earlier works in this area are based on computer vision. However, mostly these works are limited in providing real time solution for the detection activities. Therefore, we are currently working toward the Internet of Things (IoT) based solution for the human posture recognition. To achieve the objective, in the first phase, dataset of 37419 samples has been prepared consisting of healthy and unhealthy postures with sitting, standing and sleeping activities. In the second phase, this dataset will be used to classify different human postures as healthy and unhealthy using machine learning algorithm.
Each sample consists a total of 20 attributes including 9 accelerometer readings(3 readings each from knee, chest and back),9 gyroscope readings(3 readings each from knee, chest and back), activity(sitting, standing or sleeping and type(healthy or unhealthy). Figure 3 shows a small sample of the dataset. Considering the first row of the dataset, the accelerometer measure the acceleration along x, y and z axis giving 3 values at body location where sensor is placed(ax,ay,and az). Attribute chest ax=8476 gives the acceleration value along the x-axis when the sensor is placed on the chest. Similarly, chest ay=936 and chest az=252 gives the acceleration value along the y and z-axis respectively when the sensor is placed on the chest. The gyroscope measures the angular velocity along x,y and z direction(gx, gy and gz). Further, chest gx = 757 gives the angular velocity along the x-axis, chest gy=708 and chest gz=231 gives the angular velocity along the y and z-axis, when the sensor is placed on the chest. Knee ax = -124 gives the acceleration value along the x-axis, knee ay=6672 and knee az=-3560 gives the acceleration value along the y and z-axis respectively when the sensor is placed on the knee. Knee gx = -261 gives the angular velocity along the x-axis, when the sensor is placed on the knee. back ax = 324 gives the acceleration value along the x-axis when the sensor is placed on the back. Similarly, back ay=7932 and back az=2664 gives the acceleration value along the y and z-axis respectively and back gx = -496 gives the angular velocity along the x-axis, when the sensor is placed on the back.Similarly, back gy=222 and back gz=46 gives the angular velocity along the y and z-axis, when the sensor is placed on the back. Further, Type determines whether the data collected is of a healthy person or unhealthy person on the basis of posture. In our case it’s healthy. Activity determines whether the posture is of sitting, lying or standing respectively, in our case it is sitting.
Documentation
| Attachment | Size |
|---|---|
 Abstract.pdf | 444.51 KB |
Comments
I cant access the dataset ?
Sir, you have to ensure that you have to be logged in to your IEEE account, also please note, dataset is in .csv format, please have the appropriate software installed, in order to access it.
Does each row in the dataset correspond to 1 second?
No Sir, actually the dataset was created initially with a delay of 300ms to get better quality data according to our research, so the approximate time taken for us to get each reading would be instantaneous, but the time interval would be of 300ms.
Also, my sincere apologies for the late response, as I was facing some issues with my account