Sports activity recognition with UWB and inertial sensors using deep learning approach

Pająk, Iwona; Krutz, Pascal; Patalas‐Maliszewska, Justyna; Rehm, Matthias; Pająk, Grzegorz; Schlegel, Holger; Dix, Martin

doi:10.1109/fuzz-ieee55066.2022.9882654

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…A series of experiments involving a CNN with different hyperparameters allowed us to choose a classifier with one convolutional layer, five filters and a 7 × 1 kernel size. In [ 34 ], further research was conducted on decreasing computational effort by identification of significant sensor signals leading to input signal reduction. The studies focused on two issues: selecting the signals of greatest importance and sensor location.…”

Section: Resultsmentioning

confidence: 99%

“…An important problem that had to be taken into account in the studies presented in this paper was the need to use a classifier for analysis of a live stream of data. In [ 7 , 34 ], the focus was on activity detection, and the input data of the CNN classifier contained only homogeneous signals covering one type of activity in both the training and testing phases. In contrast, in the presented work, the application of a CNN classifier for analysis of ongoing registered signals was considered, so the CNN had to also properly classify heterogenous activity signals.…”

Section: Resultsmentioning

confidence: 99%

“…In this technique, signals from sensors were sliced in the time domain to form 2D windows including 50 successive samples from 6 acceleration signals. In the first case, a non-overlapping window with homogenous signals presented in [ 34 ] was used; in the second case, a successive window overlap was used that can contain heterogenous activity signals. Additionally, in both cases, the classifier was trained and tested using raw and normalized signals.…”

Section: Resultsmentioning

confidence: 99%

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Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

Patalas‐Maliszewska

Pająk

Krutz

et al. 2023

Sensors

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The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

Patalas‐Maliszewska

Pająk

Krutz

et al. 2023

Sensors

Self Cite

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“…IMU-based sports activity recognition is one of the main application fields for HAR studies, as summarized in Tables 2 and 3. It has already been proven for a variety of different sports, such as running [37][38][39], ball sports [36,[40][41][42][43][44], winter sports [45,46], sports for the disabled [47], or fitness [48][49][50][51], that activity recognition algorithms are capable of detecting specific activities tied to these sports based on IMU data as input. Other studies that incorporated IMUs focus rather on the athletes' performance [52][53][54][55] or gait estimation [56,57] than activity recognition.…”

Section: Related Work On Sports Studiesmentioning

confidence: 99%

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Hoelzemann

Romero

Bock

et al. 2023

Sensors

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We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.

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“…A precision of 94% was achieved using the CNN+LSTM model. In [59], Pozyx technology was used, which consists of mobile sensors (tags) and stationary anchors for 3D positioning through UWB communication. The TDOA and TWR algorithms were employed for positioning.…”

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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Sports activity recognition with UWB and inertial sensors using deep learning approach

Cited by 11 publications

References 17 publications

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

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

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