IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition
- Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggestRecent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.…
Verfasserangaben: | Orhan KonakORCiD, Pit Wegner, Bert ArnrichORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-487799 |
DOI: | https://doi.org/10.25932/publishup-48779 |
Titel des übergeordneten Werks (Deutsch): | Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät |
Schriftenreihe (Bandnummer): | Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät (4) |
Publikationstyp: | Postprint |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 05.01.2021 |
Erscheinungsjahr: | 2021 |
Veröffentlichende Institution: | Universität Potsdam |
Datum der Freischaltung: | 05.01.2021 |
Freies Schlagwort / Tag: | human activity recognition; image processing; machine learning; sensor data |
Ausgabe: | 4 |
Seitenanzahl: | 17 |
Quelle: | Sensors 20 (2020) 24 Art. 7179 DOI: 10.3390/s20247179 |
Organisationseinheiten: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Green Open-Access |
Lizenz (Deutsch): | CC-BY - Namensnennung 4.0 International |
Externe Anmerkung: | Bibliographieeintrag der Originalveröffentlichung/Quelle |