• M Ashish
• S Prasad Babu Vagolu
• Vedavathi Katneni

With the attractiveness of mobile sensor technology, smart wearable devices open aunmatchedoccasion to solve the puzzling human activity recognition (HAR) problematic by learning communicativesigns from the multi-dimensional daily sensor signals. This motivates us to improve a new algorithm relevant to both camera-based and wearable sensor-based HAR systems. Even though competitive classification correctness has been described, existing systems often face the encounter of distinguishing visually similar activities composed of activity patterns in different temporal orders. In this paper, we recommend a novel probabilistic procedure to efficiently encode progressive orders of activity patterns for HAR. Exactly, the algorithm studies an optimal set of latent patterns such that their progressive configurations surely matter in identifying different human activities. Then, a novel probabilistic First-Take-All (pFTA) methodology is announced to create solid features from the orders of these latent patterns to encode the entire sequence, and the temporal structural relationship between different sequences can be professionally dignified by the Hamming remoteness between compact features. Trials on three open HAR datasets display the suggestedpFTAmethodology can reach competitive presentation in relations of accuracy as well as efficiency.

Human activity recognition, temporal orders encoding, wearable sensors, learning to hash

"A study on a novel probabilistic procedure to efficiently encode progressive orders of activity patterns on Compact Features for Human Activity Recognition via Probabilistic First-Take-All ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b828-b832, June-2021, Available :http://www.jetir.org/papers/JETIR2106258.pdf

"A study on a novel probabilistic procedure to efficiently encode progressive orders of activity patterns on Compact Features for Human Activity Recognition via Probabilistic First-Take-All ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb828-b832, June-2021, Available at : http://www.jetir.org/papers/JETIR2106258.pdf