Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living : A 3D Vision Approach
Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 24(2023), 1 vom: 23. Dez. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Diraco, Giovanni [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Abnormal Gait Detection |
|---|
| Anmerkungen: |
Date Completed 12.01.2024 Date Revised 13.01.2024 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.3390/s24010082 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM366935313 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM366935313 | ||
| 003 | DE-627 | ||
| 005 | 20240114234857.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240114s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s24010082 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1258.xml |
| 035 | |a (DE-627)NLM366935313 | ||
| 035 | |a (NLM)38202944 | ||
| 035 | |a (PII)82 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Diraco, Giovanni |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living |b A 3D Vision Approach |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 12.01.2024 | ||
| 500 | |a Date Revised 13.01.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Abnormal Gait Detection | |
| 650 | 4 | |a Ambient Assisted Living | |
| 650 | 4 | |a Gated Recurrent Unit | |
| 650 | 4 | |a Long Short-Term Memory Autoencoder | |
| 650 | 4 | |a RGB-D Camera | |
| 650 | 4 | |a Temporal Convolutional Network | |
| 700 | 1 | |a Manni, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Leone, Alessandro |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 24(2023), 1 vom: 23. Dez. |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2023 |g number:1 |g day:23 |g month:12 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s24010082 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2023 |e 1 |b 23 |c 12 | ||