• Chima Godknows Igiri
• Friday Orji
• Dumnamene JS Sako
• V.I.E. Anireh

Human Activity Recognition(HAR) is behind many human-centric applications such as Smart Homes, and public healthcare. This paper presents a way of detecting six basic physical human activities (Sitting, Laying, Standing, Walking, Walking Downstairs, Walking Upstairs), with accelerometer embedded in an android smartphone. K-Nearest Neighbor (KNN) algorithm, and Relief F feature selector in WEKA Machine Learning Toolkit were adopted to recognize these activities on a publicly available dataset. The average recognition accuracy of 95.2% demonstrated the feasibility of the proposed solution, and can be considered adequate for HAR. This work is significant because it can provide contextual information about the habits of users passively.

Smartphone, Human Activity Recognition, Accelerometer, Relief, KNN, Machine Learning.

"ACTIVITY RECOGNITION USING SMARTPHONE EMBEDDED SENSORS WITH K-NEAREST NEIGHBOR ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.2326-2333, October-2020, Available :http://www.jetir.org/papers/JETIR2010301.pdf

"ACTIVITY RECOGNITION USING SMARTPHONE EMBEDDED SENSORS WITH K-NEAREST NEIGHBOR ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp2326-2333, October-2020, Available at : http://www.jetir.org/papers/JETIR2010301.pdf