Association between spectral characteristics of paraspinal muscles and functional disability in patients with low back pain: a cohort study.

OBJECTIVES: Characteristics of muscle activity, represented by surface electromyography (EMG), have revealed differences between patients with low back pain (LBP) and healthy adults; how they relate to functional and clinical parameters remains unclear. The purpose of the current study was to examine the correlation between frequency characteristics of EMG (analysed using continuous wavelet transform (CWT) analysis) and patients' self-rated score of disability. DESIGN AND SETTING: This is a case-control study with 15 patients with mechanical LBP without radicular symptoms. Patients were recruited from the orthopaedic clinic at Charing Cross Hospital. Ten healthy adults were recruited from the staff working in the hospital and associated university. Patients completed the Roland-Morris Disability Questionnaire (RMDQ) and bilateral EMG activity was obtained from erector spinae at vertebral levels L4 and T12. Subjects performed three brief maximal voluntary isometric contractions (MVICs) of the back extensors and the torque was measured using a dynamometer. CWT was applied to the EMG signals of each muscle in a 200 ms window centred around the peak torque obtained during the MVICs. The ratio (low/high frequencies) of the energy, the peak power and the frequency of the peak power were calculated for each recording site, averaged and correlated with the individual's RMDQ score. RESULTS: Patients had lower peak power (T12 and L4) and lower frequency of the peak power (at T12) than the healthy adults. Additionally, RMDQ positively correlated to the average ratio of energy at T12 (r=0.63; p=0.012), that is, greater self-rated disability corresponded to a dominant distribution of energy in the lower frequencies. CONCLUSION: The current findings reveal alterations in EMG profile and its association with self-related back pain disability, suggesting that spectral characteristics of EMG reflect muscle function.

A Thermally Powered ISFET Array for On-Body pH Measurement.

Recent advances in electronics and electrochemical sensors have led to an emerging class of next generation wearables, detecting analytes in biofluids such as perspiration. Most of these devices utilize ion-selective electrodes (ISEs) as a detection method; however, ion-sensitive field-effect transistors (ISFETs) offer a solution with improved integration and a low power consumption. This work presents a wearable, thermoelectrically powered system composed of an application-specific integrated circuit (ASIC), two commercial power management integrated circuits and a network of commercial thermoelectric generators (TEGs). The ASIC is fabricated in 0.35 m CMOS and contains an ISFET array designed to read pH as a current, a processing module which averages the signal to reduce noise and encodes it into a frequency, and a transmitter. The output frequency has a measured sensitivity of 6 to 8 kHz/pH for a pH range of 7-5. It is shown that the sensing array and processing module has a power consumption 6 W and, therefore, can be entirely powered by body heat using a TEG. Array averaging is shown to reduce noise at these low power levels to 104 V (input referred integrated noise), reducing the minimum detectable limit of the ASIC to 0.008 pH units. The work forms the foundation and proves the feasibility of battery-less, on-body electrochemical for perspiration analysis in sports science and healthcare applications.

A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring.

Electromyography analysis can provide information about a muscle's fatigue state by estimating Muscle Fibre Conduction Velocity (MFCV), a measure of the travelling speed of Motor Unit Action Potentials (MUAPs) in muscle tissue. MFCV better represents the physical manifestations of muscle fatigue, compared to the progressive compression of the myoelectic Power Spectral Density, hence it is more suitable for a muscle fatigue tracking system. This paper presents a novel algorithm for the estimation of MFCV using single threshold bit-stream conversion and a dedicated application-specified integrated circuit (ASIC) for its implementation, suitable for a compact, wearable and easy to use muscle fatigue monitor. The presented ASIC is implemented in a commercially available AMS 0.35 [Formula: see text] CMOS technology and utilizes a bit-stream cross-correlator that estimates the conduction velocity of the myoelectric signal in real time. A test group of 20 subjects was used to evaluate the performance of the developed ASIC, achieving good accuracy with an error of only 3.2% compared to Matlab.