Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

Sun, Shih-Wei; Mou, Ting Chen; Fang, Chih Chieh; Chang, Pao-Chi; Hua, Kai; Shih, Huang Chia

doi:10.3390/s19061425

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…The augmented inertial sensing data that were obtained from the accelerometer and gyro sensor modalities were resampled to 150 samples, and their sequences were segmented to six intervals [ 8 ]. On the basis of the procedures in our previous work [ 20 ], we calculated the statistical features of the modalities in each interval ( Figure 8 a). For example, in Interval 1 ( Figure 8 a), the statistical features of the accelerometer modality in the x-axis were represented by the mean

, standard deviation

, and variance

.…”

Section: Proposed Deep Learning–based Violin Bowing Action Recognimentioning

confidence: 99%

Deep Learning-Based Violin Bowing Action Recognition

Sun

Liu

Chang

2020

Sensors

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We propose a violin bowing action recognition system that can accurately recognize distinct bowing actions in classical violin performance. This system can recognize bowing actions by analyzing signals from a depth camera and from inertial sensors that are worn by a violinist. The contribution of this study is threefold: (1) a dataset comprising violin bowing actions was constructed from data captured by a depth camera and multiple inertial sensors; (2) data augmentation was achieved for depth-frame data through rotation in three-dimensional world coordinates and for inertial sensing data through yaw, pitch, and roll angle transformations; and, (3) bowing action classifiers were trained using different modalities, to compensate for the strengths and weaknesses of each modality, based on deep learning methods with a decision-level fusion process. In experiments, large external motions and subtle local motions produced from violin bow manipulations were both accurately recognized by the proposed system (average accuracy > 80%).

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, standard deviation

, and variance

.…”

Section: Proposed Deep Learning–based Violin Bowing Action Recognimentioning

confidence: 99%

Deep Learning-Based Violin Bowing Action Recognition

Sun

Liu

Chang

2020

Sensors

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“…Overall, eleven distinct wearable/IoT devices types have been evaluated for fitness assessment. The examined studies were conducted using a glove [ 38 , 57 ], wristband [ 39 , 40 , 41 , 46 , 49 , 52 , 53 ], calf band [ 42 ], bicycle [ 44 ], waistband [ 45 ], chest band [ 46 ], smartwatch [ 47 , 54 ], smartphone attached to belt [ 48 ], T-shirt [ 50 ], upper torso strap [ 51 ] and bracelet [ 53 ]. Two studies did not report any results regarding the use of wearable/IoT devices for fitness assessment [ 44 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

“…Regarding criterion C, the majority of the examined works use communication standards designed to operate in Local Area Networks (LAN) using Wi-Fi [ 47 , 48 , 49 ] and Zigbee [ 53 ]) protocols, or in a Personal Area Network (PAN) using the Bluetooth [ 41 , 46 , 53 , 54 ] and Bluetooth Low-Energy (BLE) [ 38 , 40 , 44 , 49 , 52 ]) communication protocols. Only one work uses a Wide Area Network (WAN), i.e., 4G mobile communications [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

Passos

Lopes

Clemente

et al. 2021

Sensors

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Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete’s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomarkers and/or other performance indicators that are crucial to evaluate the evolution of the athlete’s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete’s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more—and more effective—biomarkers regarding the athlete’s internal workload, which limits its wider adoption by the sports community.

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“…Many different deep learning models have been used to recognize human activities in a wide range of contexts including CNN, RNN, and particularly in the context of this chapter, LSTM networks. A very recent paper [48] proposed a baseball player behavior classification system using LSTM that accurately recognizes many baseball player behaviors. The classifier is trained on multi-modal data collected from multiple heterogeneous IoT sensors and cameras.…”

Section: Related Workmentioning

confidence: 99%

Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

Neto

Santos

Rocha

et al. 2020

Challenges and Trends in Multimodal Fall Detection for Healthcare

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Human falls are one of the leading causes of fatal unintentional injuries worldwide. Falls result in a direct financial cost to health systems, and indirectly, to society's productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goal of this study is to detect 12 human daily activities (six daily human activities, five falls, and one post-fall activity) derived from multi-modal data sources -wearable sensors, ambient sensors, and vision devices. Our proposed Bi-LSTM model leverages data from accelerometer and gyroscope sensors located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30% and 38.50%, respectively.

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Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

Cited by 7 publications

References 21 publications

Deep Learning-Based Violin Bowing Action Recognition

Deep Learning-Based Violin Bowing Action Recognition

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

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