A vision-based analysis system for gait recognition in patients with Parkinson’s disease

Cho, Chien-Wen; Chao, Wen-Hung; Lin, Sheng-Di; Chen, You‐Yin

doi:10.1016/j.eswa.2008.08.076

Cited by 120 publications

(62 citation statements)

References 18 publications

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“…The clinically meaningful indicators are further extracted by recognizing the patterns from the time series data generated by these sensors (e.g., see [4]). On the other hand, a few solutions have been proposed that use visual sensors for mobility monitoring (e.g., see [1]). While the former allows for making accurate measurements at certain parts of the body equipped with sensors, the latter is superior in the coverage of the entire body, but at lower resolutions.…”

Section: Related Workmentioning

confidence: 99%

Monitoring mobility disorders at home using 3D visual sensors and mobile sensors

Kashani

Medioni

Nguyen

et al. 2013

Proceedings of the 4th Conference on Wireless Health

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In this paper, we present PoCM 2 (Point-of-Care Mobility Monitoring), a generic and extensible at-home mobility evaluation and monitoring system. PoCM 2 uses both 3D visual sensors (such as Microsoft Kinect) and mobile sensors (i.e., internal and external sensors embedded with/connected to a mobile device such as a smartphone) for complementary data acquisition, as well as a series of analytics that allow evaluation of both archived and real-time mobility data. We demonstrate the performance of PoCM 2 with a specific application developed for freeze detection and quantification from Parkinson's Disease mobility data, as an approach to estimate the medication level of the PD patients and potentially recommend adjustments.

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Section: Related Workmentioning

confidence: 99%

Monitoring mobility disorders at home using 3D visual sensors and mobile sensors

Kashani

Medioni

Nguyen

et al. 2013

Proceedings of the 4th Conference on Wireless Health

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“…analysis of the electric signals originating in the eye) [8]. Or remote methods, such as [9], [10], but with many restrictions on patient movements and complex setups, which makes it less approachable for daily use or for unexperienced personnel. Furthermore, none of these studies analyzed movement and motor performance of specific UPDRS motor tasks.…”

Section: Introductionmentioning

confidence: 99%

Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor

Dror

Yanai

Peleg

et al. 2014

2014 IEEE 28th Convention of Electrical &Amp; Electronics Engineers in Israel (IEEEI)

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Parkinson's Disease (PD) is a degenerative disease of the central nervous system with a profound effect on the motor system. Symptoms include slowness of movement, rigidity of motion and in some patients, tremor. The severity of the disease is quantified using the Unified Parkinson Disease Rating Scale (UPDRS) which is a subjective scale performed and scored by physicians. In this work, we present an automated, objective quantitative analysis of four UPDRS motor examinations of Hand Movement and Finger Taps.For this purpose, a non-invasive system for recording and analysis of fine motor skills of hands was developed. The system is based on a simple low-cost depth acquisition sensor, similar to the second generation of Microsoft's Kinect sensor, and novel recursive self-correcting hand tracking algorithm. The system allows patients to perform test tasks in a natural and unhindered manner.The evaluation of the system was carried out on PD patients and controls. Machine Learning based classification was performed on the acquired data, followed by a decision making scheme.

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“…can increase the accuracy of gait recognition [22], and DCT (discrete cosine transform) can be used for gait pattern classification [23].…”

Section: Introductionmentioning

confidence: 99%

Extraction of bodily features for gait recognition and gait attractiveness evaluation

Hong

Kang

Price

2012

Multimed Tools Appl

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Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.

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A vision-based analysis system for gait recognition in patients with Parkinson’s disease

Cited by 120 publications

References 18 publications

Monitoring mobility disorders at home using 3D visual sensors and mobile sensors

Monitoring mobility disorders at home using 3D visual sensors and mobile sensors

Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor

Extraction of bodily features for gait recognition and gait attractiveness evaluation

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