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Human activity recognition has emerged as an active research area in recent years. With the advancement in mobile and wearable devices, various sensors are ubiquitous and widely available gathering data a broad spectrum of peoples’ daily life activities. Research studies thoroughly assessed lifestyle activities and are increasingly concentrated on a variety of sport exercises. In this paper, we examine nine sport and fitness exercises commonly conducted with sport equipments in gym, such as abdominal exercise and lat pull. We collected sensor data of 23 participants for these activities, for which smartphones and smartwatches were used. Traditional machine learning and deep learning algorithms were applied in these experiments in order to assess their performance on our dataset. Linear SVM and Naive Bayes with Gaussian kernel performs best with an accuracy of 80 %, whereas deep learning models outperform these machine learning techniques with an accuracy of 92 %.

Towards a Digital Personal Trainer for Health Clubs - Sport Exercise Recognition Using Personalized Models and Deep Learning