Wearable Inertial Sensor for Human Activity Recognition in Field Hockey: Influence of Sensor Combination and Sensor Location

Shahar, Norazman; Ghazali, Nurul Fathiah; As’ari, Muhammad Amir; Swee, Tan Tian

doi:10.1088/1742-6596/1529/2/022015

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…In contrast to the studies listed above, several publication propose hierarchical systems that deal with the data distribution in real-world scenarios but use laboratory data for evaluation [ 3 , 4 , 5 ]. Rawashdeh et al [ 3 ] detect overhead motions in a first step.…”

Section: Related Workmentioning

confidence: 99%

“…Then they distinguish baseball throws from volleyball serves with an accuracy of 94.04% in the second step in order to count straining motions for shoulder and elbow overuse injury prevention [ 3 ]. In [ 4 ], different sensor combinations and locations are analysed for HAR in field hockey. The best set up with 4 sensors results in a classification accuracy of around 97%.…”

Section: Related Workmentioning

confidence: 99%

“…Examples are IMU-based systems such as Babolat Play or Qlipp tennis sensor detecting stroke and spin types and racket speed in tennis or the Zepp Play Soccer assessing kicks and sprints in football. However, in literature automatic activity recognition algorithms are commonly developed and evaluated in laboratory settings lacking proof of transferability to real-world scenarios [ 3 , 4 , 5 ]. Thereby, limitations on two different levels of the processing pipeline, data acquisition and evaluation, can be observed.…”

Section: Introductionmentioning

confidence: 99%

“…There are several studies tackling individual aspects of the aforementioned limitations, for instance, by evaluating on competition data or using a moving window approach for evaluation to consider the final distribution of events [ 4 , 14 ]. However, to the best of our knowledge a holistic consideration of limitations is not investigated yet.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Stoeve

Schuldhaus

Gamp

et al. 2021

Sensors

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The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Stoeve

Schuldhaus

Gamp

et al. 2021

Sensors

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“…Batool et al [19] proposed Mel-frequency cepstral coefficients and statistical features to detect physical activity using accelerometer and gyroscopic sensors. Shahar et al [20] analyzed the accelerometer and gyroscope signal via sensors mounted at the chest, waist, and left and right wrists of the body. The mean, standard deviation, maximum, and minimum peak features are extracted for hockey playing activities.…”

Section: Feature Based Activity Recognition Using Wearable Sensorsmentioning

confidence: 99%

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

2020

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Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.

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A Novel CNN-SVM Hybrid Model for Human Activity Recognition

Charabi,

Abidine,

Fergani

2024

Lecture Notes in Networks and Systems

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Wearable Inertial Sensor for Human Activity Recognition in Field Hockey: Influence of Sensor Combination and Sensor Location

Cited by 7 publications

References 18 publications

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

A Novel CNN-SVM Hybrid Model for Human Activity Recognition

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