Browsing by Author "Kuo, Te-Son"

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M3S-based electrical wheelchair with head-controlled device
(2006-01-01)
Chen, Show-Hong
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Chen, Yu-Luen
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Kuo, Te-Son
;
Chu, Chiung-Yu
;
Hung, Chao-Nan
The motivation of this research is to improve the ability of ambulation for people with a certain degree of disability. The control of the wheelchair is using two tilt sensors as an input-controlling module. One of the tilt sensors detects the anterior/posterior tilting of the head and moves the wheelchair forward/backward, the other distinguishes the left/right swing of the head. In order to increase the safety of this system, the M3S protocol established by the European Commission is also applied to this research. The system based on M3S protocol has the advantage of real-time signal transmission and emergent status monitoring for SCI patients with C2-C4 level.
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
(2011-11-10)
Chen, Shih-Wei
;
Lin, Sheng-Huang
;
Liao, Lun-De
;
Lai, Hsin-Yi
;
Pei, Yu-Cheng
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.