Classifying Sport-Related Human Activity from Thermal Vision Sensors Using CNN and LSTM

Polo-Rodriguez, Aurora; Montoro-Lendínez, Alicia; Espinilla, Macarena; Medina-Quero, Javier

doi:10.1007/978-3-031-13321-3_4

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…To tackle this quandary, recent investigations have explored the utilization of low-resolution thermal cameras [25][26][27][28]. Using thermal vision sensors supported by CNN is common.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

Polo-Rodriguez

Cavallo

Nugent

et al. 2023

Preprint

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“…To tackle this quandary, recent investigations have explored the utilization of low-resolution thermal cameras [25][26][27][28]. Using thermal vision sensors supported by CNN is common.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

Polo-Rodriguez

Cavallo

Nugent

et al. 2023

Preprint

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Detection of Sets and Repetitions in Strength Exercises Using IMU-Based Wristband Wearables

Polo-Rodriguez,

Diaz-Jimenez,

Carvajal

et al. 2023

Lecture Notes in Networks and Systems

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Recognition of Sports Exercises Using Inertial Sensor Technology

Krutz¹,

Rehm

Schlegel

et al. 2023

acs

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Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work was to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing were implemented. The functionalities to be realised included the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data were used for the training of classifiers and artificial neural networks (ANN). These were iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models were finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments was also displayed graphically, which enabled statements to be made about potential causes for incorrect assignments. In this context, especially the transition areas between the classes were detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.

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Classifying Sport-Related Human Activity from Thermal Vision Sensors Using CNN and LSTM

Cited by 4 publications

References 22 publications

Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

Detection of Sets and Repetitions in Strength Exercises Using IMU-Based Wristband Wearables

Recognition of Sports Exercises Using Inertial Sensor Technology

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