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Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson ’ s disease using multiple inertial sensors

View Article: PubMed Central - PubMed

ABSTRACT

Background: Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD).

Methods: A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.

Results: Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.

Conclusions: This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

No MeSH data available.


Related in: MedlinePlus

Schematic of the TUG task and motion capture system based on IMU. a Spatial schematic of a TUG path and different transition points. Seven transitions were identified among the activities performed during a TUG. These transitions are: 1) sit-to-stand 2) stand-to-walk out 3) walk out-to-turn 4) turn-to-walk in 5) walk in-to-turn 6) turn-to-stand 7) stand-to-sit. b Diagram of the 17 IMUs and their locations on the suit. c A close-up view of the IMU on the shoulders, trunk, and hip. d Using the right-hand Cartesian coordinate system, the y-axis is aligned along the length of the IMU while the x-axis is aligned along the width of the IMU. Most IMU were positioned on the body with the y-axis aligned along the limb segment, except for the IMU on the head, where the x-axis was aligned with the rotation of the head
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Fig2: Schematic of the TUG task and motion capture system based on IMU. a Spatial schematic of a TUG path and different transition points. Seven transitions were identified among the activities performed during a TUG. These transitions are: 1) sit-to-stand 2) stand-to-walk out 3) walk out-to-turn 4) turn-to-walk in 5) walk in-to-turn 6) turn-to-stand 7) stand-to-sit. b Diagram of the 17 IMUs and their locations on the suit. c A close-up view of the IMU on the shoulders, trunk, and hip. d Using the right-hand Cartesian coordinate system, the y-axis is aligned along the length of the IMU while the x-axis is aligned along the width of the IMU. Most IMU were positioned on the body with the y-axis aligned along the limb segment, except for the IMU on the head, where the x-axis was aligned with the rotation of the head

Mentions: Participant were tested in the morning during their OFF state or at least 10 h after their last medication. Participants performed two TUG tasks, one having length of 10 m, the other 5 m. Participants performed three trials of each TUG task. Data recording started with participants in a standing position to initialize the IMUs. Participants then sat down in a armed-chair to perform the task. Participants were asked to stand up without using their arms, walk to a marker on the floor (5 m and 10 m), turn around, walk back to the chair and finally sit down (Fig. 2a). Participants were asked to perform these tasks at their own pace and no instructions were given on how sit, walk, or turn.Fig. 2


Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson ’ s disease using multiple inertial sensors
Schematic of the TUG task and motion capture system based on IMU. a Spatial schematic of a TUG path and different transition points. Seven transitions were identified among the activities performed during a TUG. These transitions are: 1) sit-to-stand 2) stand-to-walk out 3) walk out-to-turn 4) turn-to-walk in 5) walk in-to-turn 6) turn-to-stand 7) stand-to-sit. b Diagram of the 17 IMUs and their locations on the suit. c A close-up view of the IMU on the shoulders, trunk, and hip. d Using the right-hand Cartesian coordinate system, the y-axis is aligned along the length of the IMU while the x-axis is aligned along the width of the IMU. Most IMU were positioned on the body with the y-axis aligned along the limb segment, except for the IMU on the head, where the x-axis was aligned with the rotation of the head
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
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getmorefigures.php?uid=PMC5384139&req=5

Fig2: Schematic of the TUG task and motion capture system based on IMU. a Spatial schematic of a TUG path and different transition points. Seven transitions were identified among the activities performed during a TUG. These transitions are: 1) sit-to-stand 2) stand-to-walk out 3) walk out-to-turn 4) turn-to-walk in 5) walk in-to-turn 6) turn-to-stand 7) stand-to-sit. b Diagram of the 17 IMUs and their locations on the suit. c A close-up view of the IMU on the shoulders, trunk, and hip. d Using the right-hand Cartesian coordinate system, the y-axis is aligned along the length of the IMU while the x-axis is aligned along the width of the IMU. Most IMU were positioned on the body with the y-axis aligned along the limb segment, except for the IMU on the head, where the x-axis was aligned with the rotation of the head
Mentions: Participant were tested in the morning during their OFF state or at least 10 h after their last medication. Participants performed two TUG tasks, one having length of 10 m, the other 5 m. Participants performed three trials of each TUG task. Data recording started with participants in a standing position to initialize the IMUs. Participants then sat down in a armed-chair to perform the task. Participants were asked to stand up without using their arms, walk to a marker on the floor (5 m and 10 m), turn around, walk back to the chair and finally sit down (Fig. 2a). Participants were asked to perform these tasks at their own pace and no instructions were given on how sit, walk, or turn.Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD).

Methods: A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.

Results: Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.

Conclusions: This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

No MeSH data available.


Related in: MedlinePlus