Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm

Tayyip Özcan

doi:10.1007/s10586-020-03050-0

Return

Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm

2020-01-20

PRE

Tayyip Özcan *

Alper Baştürk

DOI：10.1007/s10586-020-03050-0

Original

Original request for help

Save

Abstract

En 中文

Human action recognition (HAR) is a popular subject; academic society and other stakeholders. Nowadays it has a wide-spread use; lots of practical applications such as; health, assistive living, elderly care, and so on. Both visual and sensor-based data can be used; HAR. Visual data includes video images, still images, skeleton images, etc., whilst sensor-based data is acquired as numerical data from devices such as accelerometer, gyroscope, and so on. Employed classification methods and data types are of crucial importance on HAR per; mance. In this paper, sensor data based activity recognition is per; med using stacked autoencoders (SAE). Finding near optimal accuracy results with SAEs is a challenging process if structural optimization is left to user experience. The purpose of this study is to improve the accuracy of HAR classifying methods such as SAEs using heuristic optimization algorithms. Hence the structural parameters of SAEs have been optimized using artificial bee colony optimization algorithm (ABC), genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm (PSO) and an afresh developed hybrid algorithm (hABCPSO) which includes PSO and ABC in its internal structure. Leave-one-out cross-validation (LOOCV) test method is used; validating the results. Each algorithm is per; med; 30 runs and the results of these runs are analyzed by statistical methods in detail. According to experimental results, hABCPSO supported SAE gives the minimum error and is the most robustness algorithm among the others. Obtained success rates show that the proposed SAE achieved the best accuracy rate ever on UCI human activity recognition dataset and a close result to best on wireless sensor data mining dataset with regard to LOOCV test technique.

Keywords:

Deep learning

Stacked autoencoders

Sensor-based human action recognition

Human computer interaction

Statistical analysis

Hybrid optimization

ABC

PSO

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.