Classification of human activities by smart device measurements

Mürüvvet Kalkan; Yilmaz Ar

Makale detayları

Classification of human activities by smart device measurements

Yazarlar:

Mürüvvet Kalkan | Yilmaz Ar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.166-178

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289796

pISSN:

1303-6009

Anahtar kelimeler:

Özet:

 The prevalence of activity detectors in users personal mobile devices has been incorporated into an increasing interest in research into physical function recognition HAR - Human Activity Recognition. With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.

 The prevalence of activity detectors in users personal mobile devices has been incorporated into an increasing interest in research into physical function recognition HAR - Human Activity Recognition. With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.

 The prevalence of activity detectors in users personal mobile devices has been incorporated into an increasing interest in research into physical function recognition HAR - Human Activity Recognition. With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.

 The prevalence of activity detectors in users personal mobile devices has been incorporated into an increasing interest in research into physical function recognition HAR - Human Activity Recognition. With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.

Makale detayları

Classification of human activities by smart device measurements

Yazarlar:

Mürüvvet Kalkan | Yilmaz Ar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.166-178

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289796

pISSN:

1303-6009

Human activity recognition

HAR

machine learning

accelerometer

gyroscop

Sayı

Makale detayları

Classification of human activities by smart device measurements

Yazarlar: Mürüvvet Kalkan | Yilmaz Ar

Yayıncı: Ankara University

Ana Başlıklar: Engineering

Dergi: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.166-178

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001289796

pISSN: 1303-6009

Human activity recognition

HAR

machine learning

accelerometer

gyroscop

Sayı

Yazarlar:

Mürüvvet Kalkan | Yilmaz Ar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.166-178

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289796

pISSN:

1303-6009