Application-Layer Time Synchronization and Data Alignment Method for Multichannel Biosignal Sensors Using BLE Protocol.

Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved problem. Current solutions use custom wireless protocols or extra hardware for synchronization, forming custom systems with high power consumption that prohibit migration between commercial microcontrollers. We aimed to develop a better solution. We successfully developed a low-latency, Bluetooth low energy (BLE)-based data alignment method, implemented in the BLE application layer, making it transferable between manufacturer devices. The time synchronization method was tested on two commercial BLE platforms by inputting common sinusoidal input signals (over a range of frequencies) to evaluate time alignment performance between two independent peripheral nodes. Our best time synchronization and data alignment method achieved absolute time differences of 69 ± 71 µs for a Texas Instruments (TI) platform and 477 ± 490 µs for a Nordic platform. Their 95th percentile absolute errors were more comparable-under 1.8 ms for each. Our method is transferable between commercial microcontrollers and is sufficient for many biomedical applications.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol.

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 µs, while that of the LIDA algorithm was 189.9 ± 204.7 µs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Force/moment tracking performance during constant-pose, force-varying, bilaterally symmetric, hand-wrist tasks.

Bilateral movement is widely used for calibration of myoelectric prosthesis controllers, and is also relevant as rehabilitation therapy for patients with motor impairment and for athletic training. Target tracking and/or force matching tasks can be used to elicit such bilateral movement. Limited descriptive accuracy data exist in able-bodied subjects for bilateral target tracking or dominant vs non-dominant dynamic force matching tasks requiring more than one degree of freedom (DoF). We examined dynamic trajectory (0.75 Hz band-limited, white, uniform random) constant-posture, hand open-close, wrist pronation-supination target tracking and matching tasks. Tasks were normalized to maximum voluntary contraction (MVC), spanning a ± 30% MVC force range, in four 1-DoF and 2-DoF tasks: (1, 2) unilateral dominant limb tracking with/without visual feedback, and (3, 4) bilateral dominant/non-dominant limb tracking with mirror visual feedback. In 12 able-bodied subjects, unilateral tracking error with visual feedback averaged 10-15 %MVC, but up to 30 %MVC without visual feedback. Bilateral matching error averaged âˆ¼10 %MVC and was affected little by visual feedback type, so long as feedback was provided. In 1-DoF bilateral tracking, the dominant side had statistically lower error than the non-dominant side. In 2-DoF bilateral tracking, the side providing mirror visual feedback exhibited lower error than the opposite side. In 2-DoF tasks (assumed to be more challenging than their constituent 1-DoF tracking tasks), hand grip force errors grew disproportionately larger than those of each wrist DoF. In unilateral 1-DoF tasks, both hand vs target and wrist vs target latency averaged 250-350 ms. In unilateral 2-DoF tasks, wrist vs target latency also averaged 250-350 ms, while hand vs target latency averaged > 500 ms. These results provide guidance on bilateral 2-DoF hand-wrist performance in target tracking, and dominant vs non-dominant force matching tasks.

EMG-Force and EMG-Target Models During Force-Varying Bilateral Hand-Wrist Contraction in Able-Bodied and Limb-Absent Subjects.

System identification models relating forearm electromyogram (EMG) signals to phantom wrist radial-ulnar deviation force, pronation-supination moment and/or hand open-close force (EMG-force) are hampered by lack of supervised force/moment output signals in limb-absent subjects. In 12 able-bodied and 7 unilateral transradial limb-absent subjects, we studied three alternative supervised output sources in one degree of freedom (DoF) and 2-DoF target tracking tasks: (1) bilateral tracking with force feedback from the contralateral side (non-dominant for able-bodied/ sound for limb-absent subjects) with the contralateral force as the output, (2) bilateral tracking with force feedback from the contralateral side with the target as the output, and (3) dominant/limb-absent side unilateral target tracking without feedback and the target used as the output. "Best-case" EMG-force errors averaged ~ 10% of maximum voluntary contraction (MVC) when able-bodied subjects' dominant limb produced unilateral force/moment with feedback. When either bilateral tracking source was used as the model output, statistically larger errors of 12-16 %MVC resulted. The no-feedback alternative produced errors of 25-30 %MVC, which was nearly half the tested force range of ± 30 %MVC. Therefore, the no-feedback model output was not acceptable. We found little performance variation between DoFs. Many subjects struggled to perform 2-DoF target tracking.

Efficiently Training Two-DoF Hand-Wrist EMG-Force Models.

Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently, black box models have expanded to 2-DoF tasks. To facilitate efficient training, we examined parameters of black box model training methods in 2-DoF force-varying, constant-posture tasks consisting of hand open-close combined with one wrist DoF. We found that approximately 40-60 s of training data is best, with progressively higher EMG-force errors occurring for progressively shorter training durations. Surprisingly, 2-DoF models in which the dynamics were universal across all subjects (only channel gain was trained to each subject) generally performed 15-21% better than models in which the complete dynamics were trained to each subject. In summary, lower error EMG-force models can be formed through diligent attention to optimization of these factors.