Common sport activity recognition using inertial sensor

Ghazali, Nurul Fathiah; Shahar, Norazman; Rahmad, Nur Azmina; Sufri, Nur Anis Jasmin; As’ari, Muhammad Amir; Latif, Haseeb

doi:10.1109/cspa.2018.8368687

Cited by 22 publications

(14 citation statements)

References 10 publications

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“…Medical Fitness Security Wu et al [86] Chen et al [89] Zebin et al [92] Ordóñez et al [96] Neverova et al [101] Zhen et al [104] Chen et al [105] Camps et al [108] Gharani et al [109] McGinnis et al [111] Zhao et al [116] Murad et al [119] Dehzangi et al [128] Steffan et al [130] Almaslukh et al [131] Cheng et al [133] Zdravevski et al [136] Abdulhay et al [137] Gadaleta et al [139] Xia et al [142] Asuncion et al [144] Huang et al [146] Aicha et al [147] Rescio et al [150] Hsieh et al [151] Putra et al [153] Ghazali et al [154] Rastegari et al [155] Gurchiek et al [159] Zhang et al [162] Abujrida et al [165] Kim et al [167] Wang et al [168]…”

Section: Papermentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Papermentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…Despite these added complexities, our approach obtained similar performance to what reported by the literature as the current state of the art. Ghazali et al ( 2018 ) achieved 91.2% accuracy in tracking several common sporting activities such as walking, sporting, jogging sprinting and jumping. Using wearable sensors and SVM/kNN methods, Mannini and Sabatini ( 2010 ) were able to distinguish between elementary physical activities such as standing, sitting, lying, walking, climbing and identify activities within sequences of sitting-standing-walking-standing-sitting with an accuracy between 97.8 and 98.3%.…”

Section: Discussionmentioning

confidence: 99%

“…The problem of tracking, identifying and classifying human actions has received increasing interest over the years, as it plays a key role in many applied contexts, such as human-computer interface (Popoola and Wang, 2012 ; Sarig Bahat et al, 2015 ; Quitadamo et al, 2017 ; Bachmann et al, 2018 ), daily-life activity monitoring (Mannini and Sabatini, 2010 ; Cheng et al, 2015 ; Chetty and White, 2016 ), clinical assessment (Rawashdeh et al, 2016 ; Arifoglu and Bouchachia, 2017 ; Howell et al, 2017 ) and sports performance (Attal et al, 2015 ; Ghazali et al, 2018 ; Hsu et al, 2018 ). The development of unobtrusive technologies for motion capture (e.g., wearable inertial measurement units—IMUs), their widespread integration in relatively cheap, commercially available devices (e.g., smartphones, watches, activity trackers, heart rate monitors, sensorized insoles), and the push toward healthier, more active life styles, have generated a multitude of existing and potential applications where automatic movement classification and assessment is fundamental (Attal et al, 2015 ; Cheng et al, 2015 ; Cust et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Wearable systems are particularly suitable to sport-specific needs (van der Kruk and Reijne, 2018 ), since: (1) sport usually takes place in uncontrolled and unstructured settings, with environmental conditions difficult to be predicted a priori (e.g., weather, interaction with equipment and other people) and many possible measurement interferences (e.g., electromagnetic noise); (2) the size of the acquisition volume inherently depends on the type of practiced sport (e.g., team vs. individual, indoor vs. outdoor); (3) sensors used to capture sports movements should be both robust and non-obtrusive for the athlete (i.e., ecologically transparent). Systems based on wearable devices, including low-cost activity trackers, smartwatches and smartphones (Ahmad et al, 2017 ), have kept evolving and are widely available for the consumer market, including clinical uses and sports applications (Ghazali et al, 2018 ; Hsu et al, 2018 ). Wearable technologies for motion analysis are predominantly inertial measurement units (IMUs) (Davila et al, 2017 ), which, thanks to their low cost and minimal obtrusiveness, represent an optimal solution for tracking and assessing sports movement on-field (Hsu et al, 2018 ; van der Kruk and Reijne, 2018 ; Adesida et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Preatoni

Nodari

Lopomo

2020

Front. Bioeng. Biotechnol.

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Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter-and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8-18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. Preatoni et al. Gesture Classification in Functional Training We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training.

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“…CLA had been used in classifying civet coffee with the highest accuracy of 86.5% obtained by both the quadratic support vector machine and the fine Gaussian support vector machine [7]. CLA has been used in common sports recognition using inertial sensor with 91.2% accuracy [8]. CLA has been used for classification of coffee bean species and 95.6 % accuracy was achieved using the Bagged Trees classifier [9].…”

Section: Introductionmentioning

confidence: 99%

Classification of Pepper Seeds Using Data Mining Algorithms

Dioses¹

2020

IJATCSE

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This paper is about separating the two classes of pepper seeds namely the bell pepper and the chili pepper. This paper used the same dataset from the previous study obtained using image processing. The image processing process was not repeated instead the extracted features namely the area , equivalent diameter , perimeter and roundness were used. The four features were subjected for classification using the 23 classifiers in the MATLAB Classification Learner App. Among the 23 classifiers, the best in terms of accuracy and speed is the weighted K nearest neighbor which achieved 100 % accuracy in 0.06 seconds.

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Common sport activity recognition using inertial sensor

Cited by 22 publications

References 10 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Classification of Pepper Seeds Using Data Mining Algorithms

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