DTWDIR: An Enhanced DTW Algorithm for Autistic Child Behaviour Monitoring

Slim, Salwa O.; Atia, Ayman; M.Mostafa, Mostafa Sami

doi:10.5121/iju.2016.7201

Cited by 2 publications

(1 citation statement)

References 28 publications

(33 reference statements)

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“…For example in [30], the offline classification was used for comparing seven classifiers in order to find the optimal one. Also in [31], a new classifier was developed for achieving appropriate accuracy in the child's behaviors detection. Some of researchers used offline classification for studying the efficiency of using accelerometer alone or to be combined with another sensor in the data collection phase.…”

Section: Offlinementioning

confidence: 99%

Survey on Human Activity Recognition based on Acceleration Data

Slim¹,

Atia²,

Elfattah³

et al. 2019

ijacsa

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Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambientassisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components. The state of the art in human activity recognition based on accelerometer is surveyed. According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy. The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline). Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices. Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors.

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Section: Offlinementioning

confidence: 99%