Cyclic motion analysis using inertial sensors and machine learning
Cyclic motions such as walking, running or cycling are common to our daily lives. Thus, the analysis of these cycles has an important role to play within both the medical field, e.g. gait analysis, and the fitness domain, e.g. step counting and running analysis. For such applications, inertial senso...
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| Κύριος συγγραφέας: | |
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| Μορφή: | Online |
| Γλώσσα: | Αγγλικά |
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FAU University Press
2025
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| Διαθέσιμο Online: | ONIX_20251215T160010_9783961473007_50 |
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| _version_ | 1869530604326879232 |
|---|---|
| author | Martindale, Christine |
| author_browse | Martindale, Christine |
| author_facet | Martindale, Christine |
| author_sort | Martindale, Christine |
| collection | Directory of Open Access Books |
| description | Cyclic motions such as walking, running or cycling are common to our daily lives. Thus, the analysis of these cycles has an important role to play within both the medical field, e.g. gait analysis, and the fitness domain, e.g. step counting and running analysis. For such applications, inertial sensors are ideal as they are mobile and unobtrusive. The aim of this thesis is to capture cyclic motion using inertial sensors and subsequently analyse them using machine learning techniques. A lack of realistic and annotated data currently limits the development and application of algorithms for inertial sensors under non-laboratory conditions. This is due to the effort required to both collect and label such data. The first contributions of this thesis propose novel methods to reduce annotation costs for realistic datasets, and in this manner enable the labelling of a large benchmark dataset. The applicability of the dataset is demonstrated by using it to propose and test a robust algorithm for simultaneous human activity recognition and cycle analysis. One of these methods for reducing annotation costs is then deployed to develop the first mobile gait analysis system for patients with a rare and heterogeneous disease, hereditary spastic paraplegia (HSP). Thus, machine learning algorithms which set the state-of-the-art for cycle analysis using inertial sensors were proposed and validated by this thesis. The outcomes of this thesis are beneficial in both the medical and fitness domains, enabling the development and use of algorithms trained and tested in realistic settings. |
| format | Online |
| id | doab-20.500.12854ir-170237 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1702372025-12-16T05:32:32Z Cyclic motion analysis using inertial sensors and machine learning Martindale, Christine Validierung Motion Capturing Beschleunigungssensor Gelenkkrankheit Gelenkendoprothese ´Maschinelles Lernen Ganganalyse Bewegungsstörung thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBF Medical and health informatics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general Cyclic motions such as walking, running or cycling are common to our daily lives. Thus, the analysis of these cycles has an important role to play within both the medical field, e.g. gait analysis, and the fitness domain, e.g. step counting and running analysis. For such applications, inertial sensors are ideal as they are mobile and unobtrusive. The aim of this thesis is to capture cyclic motion using inertial sensors and subsequently analyse them using machine learning techniques. A lack of realistic and annotated data currently limits the development and application of algorithms for inertial sensors under non-laboratory conditions. This is due to the effort required to both collect and label such data. The first contributions of this thesis propose novel methods to reduce annotation costs for realistic datasets, and in this manner enable the labelling of a large benchmark dataset. The applicability of the dataset is demonstrated by using it to propose and test a robust algorithm for simultaneous human activity recognition and cycle analysis. One of these methods for reducing annotation costs is then deployed to develop the first mobile gait analysis system for patients with a rare and heterogeneous disease, hereditary spastic paraplegia (HSP). Thus, machine learning algorithms which set the state-of-the-art for cycle analysis using inertial sensors were proposed and validated by this thesis. The outcomes of this thesis are beneficial in both the medical and fitness domains, enabling the development and use of algorithms trained and tested in realistic settings. 2025-12-16T05:32:31Z 2025-12-16T05:32:31Z 2025-12-15T15:04:45Z 2020 book ONIX_20251215T160010_9783961473007_50 https://library.oapen.org/handle/20.500.12657/109170 9783961473007 9783961472994 https://directory.doabooks.org/handle/20.500.12854/170237 eng FAU Studien aus der Informatik open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/109170/1/9783961473007.pdf FAU University Press 10.25593/978-3-96147-300-7 10.25593/978-3-96147-300-7 2c600dea-eece-4066-87be-da335e323fdb 9783961473007 9783961472994 201 Erlangen open access |
| spellingShingle | Validierung Motion Capturing Beschleunigungssensor Gelenkkrankheit Gelenkendoprothese ´Maschinelles Lernen Ganganalyse Bewegungsstörung thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBF Medical and health informatics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general Martindale, Christine Cyclic motion analysis using inertial sensors and machine learning |
| title | Cyclic motion analysis using inertial sensors and machine learning |
| title_full | Cyclic motion analysis using inertial sensors and machine learning |
| title_fullStr | Cyclic motion analysis using inertial sensors and machine learning |
| title_full_unstemmed | Cyclic motion analysis using inertial sensors and machine learning |
| title_short | Cyclic motion analysis using inertial sensors and machine learning |
| title_sort | cyclic motion analysis using inertial sensors and machine learning |
| topic | Validierung Motion Capturing Beschleunigungssensor Gelenkkrankheit Gelenkendoprothese ´Maschinelles Lernen Ganganalyse Bewegungsstörung thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBF Medical and health informatics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general |
| topic_facet | Validierung Motion Capturing Beschleunigungssensor Gelenkkrankheit Gelenkendoprothese ´Maschinelles Lernen Ganganalyse Bewegungsstörung thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBF Medical and health informatics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general |
| url | ONIX_20251215T160010_9783961473007_50 |
| work_keys_str_mv | AT martindalechristine cyclicmotionanalysisusinginertialsensorsandmachinelearning |