In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand's side during the wireless transmission, the compensator is restructured into a Hammerstein-Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove's and the bionic hand's dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein-Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human-robot collaboration with the bionic hand.
BACKGROUND: Knee laxity measurements have been shown to be associated with some medical conditions such as chronic joint pain and collagen tissue diseases. The aim of this study was to determine the effects of demographic factors and anthropometric measures on knee laxity.
MATERIALS AND METHODS: Data were collected from 521 visitors, staffs and students from the University Malaya Medical Centre and University of Malaya between December 2009 and May 2010. Knee laxity was measured using a KT-1000 arthrometer. Multiple regression analysis was used to find the association of knee laxity with age and anthropometric measures.
RESULTS: Using ANOVA, knee laxity did not show significant differences among ethnic groups for both genders. The average knee laxity in men was 3.47 mm (right) and 3.49 mm (left); while in women were 3.90 mm (right) and 3.67 mm (left). Knee laxity in women was significantly higher (right knee p<0.01 and left knee p<0.05) than men. Right knee laxity of men was negatively associated with height (p<0.05) and BMI (p<0.05); also a negative association was observed between left knee laxity and BMI (p<0.05). Overweight and obese men had less knee laxity than normal weight and underweight individuals. Elderly men and women (age 55 and above) had lower knee laxity (p<0.01) than young adults (ages 21-39).
CONCLUSION: These results suggest that age and body size are important factors in predicting knee laxity.
KEYWORDS: Age; Anthropometric measures; Joint mobility; KT 1000; Knee laxity