Determining the Single Best Axis for Exercise Repetition Recognition and Counting on SmartWatches

Mortazavi, Bobak J.; Pourhomayoun, Mohammad; Alsheikh, Gabriel; Alshurafa, Nabil; Lee, Sunghoon Ivan; Sarrafzadeh, Majid

doi:10.1109/bsn.2014.21

Cited by 58 publications

(45 citation statements)

References 29 publications

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“…Data from this study were used to train and test five well-known statistical machine learning algorithms: C4.5, CART, naïve Bayes, multilayer perceptrons and finally support vector machines. Mortazavi et al [4] introduced a framework for platform creation (e.g. accelerometer only system versus accelerometer and gyroscope) and machine learning of some activities, which can be especially useful in the emerging market of smartwatches.…”

Section: Related Workmentioning

confidence: 99%

“…Because of equipped with various on-board sensors, smartphones and wrist-worn devices such as smartwatches are being extensively used for activity recognition in recent studies [3]. With the popularity of the smartwatches, wrist-worn sensor devices will become an increasingly important tool in personal health monitoring [4]. Statistical learning methods are generally used in activity recognition studies.…”

Section: Introductionmentioning

confidence: 99%

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The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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“…그리고 서포트 벡터 머 신을 통해 생성된 특징점들을 이용해 피크 검출 방식으로 반 복적인 운동을 세었다. Mortazavi는 반복적인 운동을 수행할 때, 가장 영향력 있는 한 축을 선택하여 전처리와 특징점 추 출 시 계산량을 효과적으로 줄여줄 수 있는 카운트 알고리즘 을 제안하였다 [11]. 하지만 카운트를 위한 특징점을 추출할 때 앞서 말한 패턴인식 방법은 센서 미가공 데이터를 그대로 사용하여 생성된 특징점들이 직관적이지 않다.…”

Section: 서론 기술의 발달로 인구가 고령화됨에 따라[12] 규칙적인 운 동을 통해 건강을 유지하고 질병을 예unclassified

Navigation based Motion Counting Algorithm for a Wearable Smart Device

Park¹,

Lee²,

Song³

et al. 2015

Journal of Institute of Control, Robotics and Systems

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Abstract:In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.

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“…Statistical features, which are widely used in the field of HAR (i.e., mean, standard deviation, min and max values, RMS values, Pearson correlation coefficients, FFT coefficients and entropy values), were computed from each window [4,7,[9][10][11]. A feature vector comprising of n=24 features, is computed and extracted from each 4 second window.…”

Section: Feature Extractionmentioning

confidence: 99%

Activity Recognition of Local Muscular Endurance (LME) Exercises Using an Inertial Sensor

Prabhu

Ahmadi

O’Connor

et al. 2017

Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017)

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Abstract. In this paper, we propose an algorithmic approach for a motion analysis framework to automatically recognize local muscular endurance (LME) exercises and to count their repetitions using a wristworn inertial sensor. LME exercises are prescribed for cardiovascular disease rehabilitation. As a technical solution, we propose activity recognition based on machine learning. We developed an algorithm to automatically segment the captured data from all participants. Relevant time and frequency domain features were extracted using a sliding window technique. Principal component analysis (PCA) was applied for dimensionality reduction of the extracted features. We trained 15 binary classifiers using support vector machine (SVM) to recognize individual LME exercises, achieving overall accuracy of more than 98%. We applied grid search technique to obtain the optimal SVM hyperplane parameters. The learning curves (mean ± stdev) for each model is investigated to verify that the models were not over-fitted and performed well on any new test data. Also, we devised a method to count the repetitions of the upper body exercises.

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Determining the Single Best Axis for Exercise Repetition Recognition and Counting on SmartWatches

Cited by 58 publications

References 29 publications

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Navigation based Motion Counting Algorithm for a Wearable Smart Device

Activity Recognition of Local Muscular Endurance (LME) Exercises Using an Inertial Sensor

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