In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.
In order to evaluate the ability of human standing balance scientifically, we in this study proposed a new evaluation method based on the chaos nonlinear analysis theory. In this method, a sinusoidal acceleration stimulus in forward/backward direction was forced under the subjects' feet, which was supplied by a motion platform. In addition, three acceleration sensors, which were fixed to the shoulder, hip and knee of each subject, were applied to capture the balance adjustment dynamic data. Through reconstructing the system phase space, we calculated the largest Lyapunov exponent (LLE) of the dynamic data of subjects' different segments, then used the sum of the squares of the difference between each LLE (SSDLLE) as the balance capabilities evaluation index. Finally, 20 subjects' indexes were calculated, and compared with evaluation results of existing methods. The results showed that the SSDLLE were more in line with the subjects' performance during the experiment, and it could measure the body's balance ability to some extent. Moreover, the results also illustrated that balance level was determined by the coordinate ability of various joints, and there might be more balance control strategy in the process of maintaining balance.
ObjectiveTo investigate the cl inical characteristics, diagnosis, and treatment of metacarpophalangeal (MCP) joint locking with extension lag. MethodsBetween February 2009 and April 2014, 17 patients (17 fingers) with MCP joint locking with extension lag were treated. The patients included 4 males and 13 females, and the average age was 40.7 years (range, 20-72 years). The index finger was locked in 12 cases and the middle finger in 5 cases. All patients could not fully extend the MCP joint at about 30° flexion without flexion limitation of the interphalangeal joint. The range of motion (ROM) of the MCP joint was (41.2±5.1)°. The visual analogue scale (VAS) score was 2.7±0.5. X-ray and CT scanning showed that there was a bony prominence on radial condyle of the metacarpal head in 15 primary patients, and a hook like osteophyte on ulnar condyle in 2 degenerative patients. All patients were treated with close reduction first, and open reduction was conducted when the manipulation failed. ResultsSuccessful close reduction was achieved in 5 cases, and successful open reduction in 8 cases; 4 cases gave up treatment after failure for close reduction. All patients who achieved successful reduction were followed up 2.3 years on average (range, 6 months to 5 years and 2 months). The ROM of the MCP joint was increased to (80.4±6.6)° at last follow-up, showing significant difference when compared with ROM before reduction (t=-19.46, P=0.00). The VAS score decreased to 0.2±0.4 at last follow-up, also showing significant difference when compared with score before reduction (t=13.44, P=0.00). ConclusionAccessory collateral ligament caught at the bony prominence on the radial condyle of the metacarpal head is the most common cause of the MCP joint locking with extension lag. Close reduction is feasible, but recurrence of locking is possible. Surgical treatment is advised in the event of manipulation failure or recurrent locking.