Development of a Portable Chronic Non-specific Low Back Pain Measurement System / 中国医疗器械杂志

We developed a portable non-specific low back pain measurement system EasiLBP and evaluated its performance in collecting EMG signals:during the wearer's movement without the assistance of a doctor, the collection of EMG signals by portable devices met problems such as large noise interference, difficulty in accurately calibrating the start and end points of the action interval, and imbalanced samples for feature recognition, et al. To challenge these problems, we proposed a small group-based noise removal method, a dynamic dual-threshold automatic method for identifying the start and end points of the motion interval, and a sampling method to balance group samples, respectively. Portable device and a medical EMG acquisition equipment Thought Technology FlexComp Infiniti 10 were used to perform EMG measurements on 15 patients with non-specific low back pain and 15 normal people. Clinical experiments and statistical analysis show that the portable EMG acquisition system has significant differences in EMG signal characteristics between normal people and non-specific low back pain patients, and it has good measurement consistency and accuracy with the medical EMG acquisition equipment.

Simultaneous monitoring of motion ECG of two subjects using Bluetooth Piconet and baseline drift

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.