Extracting a diagnostic gait signature

Lakany, Heba

doi:10.1016/j.patcog.2007.11.004

Cited by 71 publications

(28 citation statements)

References 29 publications

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“…Gait abnormalities can also be present in the elderly due to the ageassociated decline in musculoskeletal and neurological functions, leading to a clinical profile ranging from fast short steps to a shuffling gait like that seen in PD (Plotnik et al, 2007;Rubino et al, 1993). The spatial and temporal parameters of gait abnormalities in PD (Hausdorff et al, 2009) have already been studied using different techniques, such as fractal analysis, coefficient of variation analysis (Bello et al, 2010) and variability analysis based on complex wavelet analysis (Lakany et al, 2008). These techniques provide a single value for a spatiotemporal event that occurs during the gait cycle.…”

Section: Introductionmentioning

confidence: 99%

Gait analysis and clinical correlations in early Parkinson�s disease

Pistacchi¹

2017

199

106

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SummaryThe aim of our study was to identify and quantify spatiotemporal and kinematic gait parameters obtained by 3D gait analysis (GA) in a group of Parkinson's disease (PD) patients compared with healthy subjects in order to investigate whether early PD patients could present an abnormal gait pattern. Forty-four patients affected by early-stage PD compared with a control group were analyzed. All participants were evaluated with 3D GA in the gait laboratory. The greatest significance in temporal parameters was found in cadence (102.46 ± 13.17 steps/min in parkinsonian patients vs 113.84 ± 4.30 steps/min in control subjects), followed by stride duration (1.19 ± 0.18 seconds right limb and 1.19 ± 0.19 seconds left limb in PD patients vs 0.426 ± 0.16 seconds right limb and 0.429 ± 0.23 seconds left limb in normal subjects) and stance duration. Marked differences were also found in the swing phase and in swing duration (p<0.05), while the stance phase was not significantly different in patients compared with healthy subjects. A statistically different velocity in PD patients (0.082 ± 0.29 m/s) vs healthy subjects (1.33 ± 0.06 m/s) was shown by spatial parameter analysis.Step width, stride length and swing velocity were highly significant parameters, as was average velocity. Our study highlighted some distinguishing characteristics of gait in early PD. Ambulation disorders may be present in the early stage of PD and their detection allows for early medical treatment and possible rehabilitation.

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Section: Introductionmentioning

confidence: 99%

Gait analysis and clinical correlations in early Parkinson�s disease

Pistacchi¹

2017

199

106

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“…They commonly provide data for human experts to evaluate, but they can also be used automatically [7]. In distinguishing between health problems such as hemiplegia and diplegia, the accuracy of 92.5 % was reported.…”

Section: Related Workmentioning

confidence: 99%

Behavior Analysis Based on Coordinates of Body Tags

Luštrek

Kaluža

Dovgan

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93 % at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90 %.

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“…Using similar motion-capture system as that in our approach, the automatic distinguishing between health problems such as hemiplegia and diplegia is presented in [15] using Self-Organizing Maps, whose features were wavelet-transformed gait characteristics such as walking speed and stride length.…”

Section: Introductionmentioning

confidence: 99%

Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly

Pogorelc

Gams

2010

2010 IEEE International Conference on Data Mining Workshops

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We propose a medically driven data mining application: system for diagnosing of gait patterns related to health problems of elderly to support their independent living. Gait of elderly is captured with motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine learning algorithms in order to recognize the specific health problem. We propose novel features for training a machine learning classifier that classifies the user's gait into: i) normal, ii) with hemiplegia, iii) with Parkinson's disease, iv) with pain in the back and v) with pain in the leg. Experimental results showed that decision tree classifier was able to reach only 95 % of classification accuracy, using 7 tags and 5 mm standard deviation of noise. On the other hand, k-nearest neighbor was much more accurate since it reached classification accuracy of over 99 %, using only 8 tags with 0-20 mm noise.

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Extracting a diagnostic gait signature

Cited by 71 publications

References 29 publications

Gait analysis and clinical correlations in early Parkinson�s disease

Gait analysis and clinical correlations in early Parkinson�s disease

Behavior Analysis Based on Coordinates of Body Tags

Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly

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