Your personal movie producer

Schuldhaus, Dominik; Jakob, Carolin; Zwick, Constantin; Koerger, Harald; Eskofier, Bjoern M.

doi:10.1145/2971763.2971772

Cited by 6 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In HAR external sensors are used to detect and analyse various human movements as, amongst others, walking, running, sleeping, cooking or driving [17][18][19][20]. Thereby ap-plication areas are manifold covering amongst others healthcare monitoring [21], sports performance evaluation [13,22], smart home [23,24] and autonomous driving [20,25]. Depending on the external sensor source, video-based and sensor-based HAR systems are distinguished.…”

Section: Related Workmentioning

confidence: 99%

“…The identification of shots was not covered in their work [30]. Schuldhaus et al [13] developed a hierarchical pipeline based on IMU data for the detection of full-instep and side-foot kicks. For full-instep kicks, a mean sensitivity of 95.6% is reported.…”

Section: Towards Real-world Sensor-based Activity Recognition In Field Sportsmentioning

confidence: 99%

“…In the evaluation step, the algorithms are commonly tested on segmented samples of the sensor signal containing the target movements and preselected null class instances [7,13]. Thus, the influence of confounding movements is neglected, although they might produce similar sensor output as the sport-specific movements under investigation.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work already showed the feasibility to classify full-instep kicks and side-foot kicks in laboratory settings applying a SVM classifier. However, the number of participants was limited to eleven male amateur players, data was recorded during controlled exercises, performance was evaluated on segmented samples and the null class was limited to dribbling instances [13].…”

Section: Introductionmentioning

confidence: 99%

“…Building on the findings of Schuldhaus et al [13] our research contributes in the following ways: First, we evaluated the applicability of the reported features and SVM classifier for shot and pass detection versus a null class in real-world scenarios. Second, we analysed the potential of improving classification performance by using and comparing different deep learning architecture types.…”

Section: Introductionmentioning

confidence: 99%

See 4 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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Towards Real-world Sensor-based Activity Recognition In Field Sportsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

SoccerMate: A personal soccer attribute profiler using wearables

Hossain

Khan

Roy

2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

View full text Add to dashboard Cite

The use of smartphone and wearable devices in various sporting events is an optimistic opportunity to profile player's physical fitness and physiological health conditioning attributes. Recently a variety of commercial wearables with respect to different sports are available in the market. As these wearables differ for distinctive sports, it becomes a hassle to effectively profile them for multiple sports sessions in day to day practice events. Wrist worn devices like smartwatches are becoming a trend in sports analytics recently and researchers are leveraging them to infer various contexts of the players to improve the quality, tactics, strategy of playing matches against the opponents. Visual observation is the most popular way to track a player's abilities in soccer, but as a player it is not always possible to self-assess your own strengths and weaknesses in a field. In this paper, we propose to exploit the wrist worn devices with built in accelerometer to help represent attributes of technical judgement, tactical awareness and physical aspects of a soccer player. We propose to use deep learning to build our classification model which analyzes different soccer events like in-possession, pass, kick, sprint, run and dribbling. Based on these soccer events, we evaluate the overall ability of a soccer player. Our experiments show that, these wearable technology guided attributes profiling can help a coach or scout to better understand the competence of a player in addition to traditional visual observation.

show abstract

AgileRabbit

Liu

et al. 2017

Proceedings of the 9th Asia-Pacific Symposium on Internetware

View full text Add to dashboard Cite

Your personal movie producer

Cited by 6 publications

References 13 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

SoccerMate: A personal soccer attribute profiler using wearables

AgileRabbit

Contact Info

Product

Resources

About