Search

This chapter presents an elderly fall-detection monitoring solution that implements both accelerometer and sound-based detection algorithm. The accelerometer-based fall detection is instrumental in the detection of a valid fall occurrence. However, it has been shown that using accelerometer alone is insufficient to accurately detect a fall, as the accelerometer tends to misinterpret some of the daily motion activities and misclassified them as valid falls. The sound sensor is introduced to detect the sound pressure generated from a resultant fall, but sound pressure cannot by itself be used as a reliable indicator of a fall. Thus a fuzzy logic–based fall-detection algorithm is developed to process the output signals from the accelerometer and sound sensor, where a valid fall activity detected by the accelerometer, coupled with a detected sound pressure from the resultant fall, can infer an occurrence of a valid fall. This chapter demonstrates the proposed algorithm is able to improve the accuracy of detecting a valid fall as compared to the accelerometer only fall-detection algorithm by minimizing false fall detections per day from high of 1.37 to low of 0.06.

In this paper, fault diagnosis and accommodation control are developed for robotic systems. First, a nonlinear observer in the proposed method is designed based on the available model. The fault detection is carried out by comparing the observer states with their signatures. Secondly, state observers are constructed based on possible fault function sets. Thirdly, the accommodation control design is developed using a normal controller plus a neural network compensator to capture the nonlinear characteristics of faults. Finally, if the fault isolation is completed successfully, the second fault accommodation controller is presented based on the fault information obtained by the isolation scheme.