weSport: Utilising wrist-band sensing to detect player activities in basketball games

Bai, Lu; Efstratiou, Christos; Ang, Chee Siang

doi:10.1109/percomw.2016.7457167

Cited by 31 publications

(38 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another authors [46] also developed a method to identify the activity related to shooting with the implementation of Random Forest, SVM and KNN methods, using the mean, standard deviation, median,…”

mentioning

confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

View full text Add to dashboard Cite

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%. Following the previous research studies [4][5][6], the recognition of the ADL is composed by several steps, such as the data acquisition, the data processing, composed by data cleaning, data imputation, and feature extraction, the data fusion, and the artificial intelligence methods for the concrete identification of the ADL. However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing. The datasets are composed with raw accelerometer data acquired by individual with ages ranged between 16 and 60 years old and different lifestyles, with a mobile device in the pocket. For the implementation of these types of ANN are used several datasets with different sets of features, identifying the best features to increase the accuracy of the recognition, and three Java libraries are used for the implementation of the different methods, such as Neuroph [13], Encog [14], and DeepLearning4j [15], achieving the best accuracy with Deep Learning methods.The remaining sections of this paper are organized as follows: Section 2 presents a brief literature review re...

show abstract

mentioning

confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Treadmill screens can also provide real-time feedback to runners [CMHD10]. The use of small, portable devices such as wrist-bands to detect activity in basketball [BEA16] also provides individual feedback. Equipment can also be used as a ubiquitous mechanism to broadcast gamerelated data, such as heart rate data on a bicycle helmet [WWM13].…”

Section: Players' Point Of Viewmentioning

confidence: 99%

“…These include algorithms, which can help with gathering new tracking data (e.g. [BEA16]), computing new metadata (e.g., [FMBG15,DWA10]), and more generally compute new derived data by aggregating existing data (e.g., [AAB * 16, BJM02, MCHD12]). Algorithms can also help model sports, usually by leveraging expert knowledge (e.g., [CPG * 16, SHJ * 15]).…”

Section: Contributions and Evaluationsmentioning

confidence: 99%

State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

View full text Add to dashboard Cite

In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis-identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.

show abstract

“…The activity tracking system by Lu Bai which is used to detecting the player's activities within sports. Through the inbuilt sensor of wristband and Smartphones, the tracking system can capture the game attempt of each player and provide performance analysis report about players [10]. Based on the classifier available in this tracking system we can achieve the accuracy of attempt of player in every games.…”

Section: Lifestyle and Healthcarementioning

confidence: 99%

A demand for wearable devices in health care

Vijayalakshmi¹,

Uma²,

Bhuvanya³

et al. 2018

IJET

View full text Add to dashboard Cite

With the popularity of wearable devices, along with the development of telecommunication system there is a need for obtaining the health and fitness outcomes. So the recent advances in data analysis techniques have opened up new possibilities for using wearable technology in the digital health ecosystem. In past, it's too difficult to use the wearable devices for healthcare system because of the size of those sensors. But now with front end amplification and wireless data transmission, the wearable devices are deployed in health monitoring systems. Although the devices are continuously monitoring the human's body activity and collect various physiological data to increase the quality of human's life. In this paper first we provide a research survey on available wearable or gadgets. Also we conclude with future directions in wearable research and market.

show abstract

weSport: Utilising wrist-band sensing to detect player activities in basketball games

Cited by 31 publications

References 16 publications

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

State of the Art of Sports Data Visualization

A demand for wearable devices in health care

Contact Info

Product

Resources

About