A summative scoring system for evaluation of human kinematic performance

Evaluation of human kinematic performance is essential in rehabilitation and skill assessment. These services are in high demand where the improvements made due to exercises need to be regularly assessed. In some relevant industries there is a need to evaluate their employee capabilities quantitatively for accident compensation and insurance purposes. In particular, these assessments are preferred to be based on more quantifiable measures in a standardized form ensuring accuracy, reliability, ease of use and anywhere anytime information to the clinician. Therefore, it is necessary to have an efficient mechanism for evaluation and assessment of human kinematic movements as the current motion matching and recognition algorithms fall short due to characteristically strict specifications required in numerous health care applications. In this paper, we propose a summative approach using a double integral to define a closeness between two trajectories typically generated by human movement. This approach can be considered as a spatial scoring mechanism in the evaluation of human kinematic performance as well as in movement recognition applications. Several experiments based on computer simulations as well as real data were set up to examine the performance of the proposed approach as a scoring mechanism for the evaluation of human kinematic performances. The results demonstrated better characterization of the movement assessment and motion recognition ability, with a recognition rate of 86.19%, than the currently used methods such as Gaussian mixture models and pose normalization employed in motion recognition tasks. Finally, we use the scoring mechanism to analyze the proximity in human kinematic performance.

Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. © 2006 IEEE.

Learning stable weights for data of varying dimension

In this paper we develop a data-driven weight learning method for weighted quasi-arithmetic means where the observed data may vary in dimension.