Investigating optimal smartphone placement for identifying stairs movement using machine learning
The identification of human activities such as stair ascending and descending poses a significant challenge due to the proximity of data provided by the sensory pathway. Accurate identification of human activities is crucial in conveying essential gait information to users for the recognition of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Deer Hill Publications
2023
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/113421/7/113421_Investigating%20optimal%20smartphone.pdf http://irep.iium.edu.my/113421/ https://www.deerhillpublishing.com/index.php/ijemm/article/view/261 https://doi.org/10.26776/ijemm.08.04.2023.02 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The identification of human activities such as stair ascending and descending poses a significant challenge due to the
proximity of data provided by the sensory pathway. Accurate identification of human activities is crucial in conveying
essential gait information to users for the recognition of human movement activities. However, gait patterns can vary
significantly between individuals, making it challenging to develop a generalized algorithm for identifying incline
surface human activity. Factors such as walking speed, stride length, and body mechanics influence gait patterns,
making it difficult to establish a consistent framework. Despite various research on gait event detection for level
ground walking, the identification of gait activities on an inclined surface such as stairs, especially using smartphones
as sensors, is currently lacking. The goal of this study is to investigate and develop a reliable and accurate method for
detecting gait activities on an inclined surface such as stairs using smartphones as the sensing device. Specifically, this
study focuses on investigating the optimal placement of smartphones to extract tri- axis accelerometer data from the
inertial sensors during stair movement. The inertial sensor data was collected from the smartphone at two different
positions and two different orientations. The data was trained against 6 machine learning algorithms namely Decision
Tree, Logistic Regression, Naive Bayes, Random Forest, Neural Networks and KNN. It was observed that, by using
the Decision Tree and Random Forest algorithm 100% accuracy was achieved, when the smartphone was placed at
the thigh during stair movement. Successful identification of stair movement activity by using a smartphone can
significantly contribute to future research and could also prove useful to the wider community such as amputees and
those with pathological gait. In addition, since smartphones are available to a wide group of people, a low-cost
solution for human activity identification can be realized, without requiring the use of external sensors and circuitry. |
|---|