Using Wrist-Worn Activity Recognition for Basketball Game Analysis

Hölzemann, Alexander; Laerhoven, Kristof Van

doi:10.1145/3266157.3266217

Cited by 29 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thirteen studies reported or displayed classification matrices so the reader could calculate more than one evaluation metric. 6,7,13,15,[17][18][19][20][22][23][24][25] Depending on the nature of the classification problem, reporting only accuracy can lead to misrepresentation of the results; data with imbalanced classes (e.g. a low number of observations in one activity class relative to another) can lead to inflated overall accuracy.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…30 Two studies 3,20 used data from all three sensors, while the remaining studies used either a combination of the accelerometer and gyroscope 7,[11][12][13][14][15][16][17][18][19]21,22,25,32 or only the accelerometer. 4,6,23,24 Only one study investigated different combinations of sensors for classification of tennis strokes. 3 When used separately, the accelerometer showed slightly higher classification accuracy (79%) compared to gyroscope (76%) and magnetometer data (76%).…”

Section: Sensor Modalitymentioning

confidence: 99%

“…Fourteen studies specified the number of features used, ranging from 4 to~2000 features. Eight studies used only time-domain features, [10][11][12][13]15,19,24,25 while seven studies used a combination of features in the time and frequency domain. 4,7,16,17,20,22,23 No studies compared whether time or frequency domain features were more important for classifying certain movements; however, a broad range of features from both domains are likely to be beneficial.…”

Section: Pre-processingmentioning

confidence: 99%

See 2 more Smart Citations

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

McGrath

Neville

Cronin

2020

Proceedings of the Institution of Mechanical Engineers, Part P:

View full text Add to dashboard Cite

Inertial measurement units (IMUs) are becoming increasingly popular in activity classification and workload measurement in sport. This systematic literature review focuses on upper body activity classification in court or field-based sports. The aim of this paper is to provide sport scientists and coaches with an overview of the past research in this area, as well as the processes and challenges involved in activity classification. The SPORTDiscus, PubMed and Scopus databases were searched, resulting in 20 articles. Both manually defined algorithms and machine learning approaches have been used to classify IMU data with varying degrees of success. Manually defined algorithms may offer simplicity and reduced computational demand, whereas machine learning may be beneficial for complex classification problems. Inter-study results show that no one machine learning model is best for activity classification; differences in sensor placement, IMU specification and pre-processing decisions can all affect model performance. Accurate classification of sporting activities could benefit players, coaches and team medical personnel by providing an objective estimate of workload. This could help to prevent injuries, enhance performance and provide valuable data to coaching staff.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Sensor Modalitymentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

McGrath

Neville

Cronin

2020

Proceedings of the Institution of Mechanical Engineers, Part P:

View full text Add to dashboard Cite

show abstract

“…Inertial measurement units (IMU) are packages that combine multi-axis acceleration and gyroscope sensors to measure motion or the direction of movement [1]. They are frequently used in advanced consumer electronics, such as wearable devices and smartphones, for games [2] and healthcare applications [3,4,5], but also in robots for industrial applications [6,7]. Contrary to environmental sensors, the output of acceleration and gyroscope sensors often requires calibration and further signal processing to elicit relevant features to automate decisions or inform their users.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

hamme

Garofalo

Rúa

et al. 2019

Sensors

View full text Add to dashboard Cite

Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

show abstract

“…Human Activity Recognition (HAR) is the task of classifying human movements. HAR methods became relevant in applications such as mobile-or ambient-assisted living, smart-homes, rehabilitation, health support, and industrial settings [11][12][13][14]. HAR commonly processes signals from videos, Motion Capturing (MoCap) systems, a set of on-body sensors or other data sources [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

View full text Add to dashboard Cite

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

show abstract

Using Wrist-Worn Activity Recognition for Basketball Game Analysis

Cited by 29 publications

References 16 publications

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Contact Info

Product

Resources

About