Human Activity Prediction using Long Short Term Memory

Septiadi, Jaka; Warsito, Budi; Wibowo, Adi

doi:10.1051/e3sconf/202020215008

Cited by 2 publications

(1 citation statement)

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“…Another popular method is Sequence mining that can be used to address such problems [27]. A dataset consisting the name of activities was generated from a collection of human actions using mapping and word embedding using LSTM algorithms to predict the future activity was implemented in [28]. Some activity prediction works are also found to be vision-based [7], [29], [2].…”

Section: Literature Reviewmentioning

confidence: 99%

User-centric Activity Recognition and Prediction Model using Machine Learning Algorithms

Roy¹,

Ahmed²,

Huq³

et al. 2021

IJACSA

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Human Activity Recognition has been a dynamic research area in recent years. Various methods of collecting data and analyzing them to detect activity have been well investigated. Some machine learning algorithms have shown excellent performance in activity recognition, based on which many applications and systems are being developed. Unlike this, the prediction of the next activity is an emerging field of study. This work proposes a conceptual model that uses machine learning algorithms to detect activity from sensor data and predict the next activity from the previously seen activity sequence. We created our activity recognition dataset and used six machine learning algorithms to evaluate the recognition task. We have proposed a method for the next activity prediction from the sequence of activities by converting a sequence prediction problem into a supervised learning problem using the windowing technique. Three classification algorithms were used to evaluate the next activity prediction task. Gradient Boosting performs best for activity recognition, yielding 87.8% accuracy for the next activity prediction over a 16-day timeframe. We have also measured the performance of an LSTM sequence prediction model for predicting the next activity, where the optimum accuracy is 70.90%.

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Section: Literature Reviewmentioning

confidence: 99%