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.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback.

Most transradial prosthesis users with conventional "Sequential" myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: "Intuitive" control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and "Mapping" control (wrist flexion-extension and ulnar-radial deviation controlled virtual target left-right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being "matched," and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4-7 vs. 2 matches, p< 0.001) and throughput (average of 0.75-1.25 vs. 0.4 bits/s, p< 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.

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.

Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8-18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2-4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.

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.

Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes.

Few studies have related the surface electromyogram (EMG) of forearm muscles to two degree of freedom (DoF) hand-wrist forces; ones that have, used large high-density electrode arrays that are impractical for most applied biomechanics research. Hence, we researched EMG-force in two DoFs-hand open-close paired with one wrist DoF-using as few as four conventional electrodes, comparing equidistant placement about the forearm to optimized site selection. Nine subjects produced 1-DoF and 2-DoF uniformly distributed random forces (bandlimited to 0.75 Hz) up to 30% maximum voluntary contraction (MVC). EMG standard deviation (EMGσ) was related to force offline using linear dynamic regression models. For 1-DoF forces, average RMS errors using two optimally-sited electrodes ranged from 8.3 to 9.0 %MVC, depending on the DoF. For 2-DoFs, overall performance was best when training from both 1- and 2-DoF trials, giving average RMS errors using four optimally-sited electrodes of 9.2 %MVC for each DoF pair (hand open-close paired with one wrist DoF). For each model, additional optimally-sited electrodes showed little statistical improvement. Electrodes placed equidistant performed noticeably poorer than an equal number of electrodes that were optimally sited. The results suggest that reliable 2-DoF hand-wrist EMG-force with a small number of electrodes may be feasible.

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes.

Surface electromyogram-controlled powered hand/wrist prostheses return partial upper-limb function to limb-absent persons. Typically, one degree of freedom (DoF) is controlled at a time, with mode switching between DoFs. Recent research has explored using large-channel EMG systems to provide simultaneous, independent and proportional (SIP) control of two joints-but such systems are not practical in current commercial prostheses. Thus, we investigated site selection of a minimum number of conventional EMG electrodes in an EMG-force task, targeting four sites for a two DoF controller. In a laboratory experiment with 10 able-bodied subjects and three limb-absent subjects, 16 electrodes were placed about the proximal forearm. Subjects produced 1-DoF and 2-DoF slowly force-varying contractions up to 30% maximum voluntary contraction (MVC). EMG standard deviation was related to forces via regularized regression. Backward stepwise selection was used to retain those progressively fewer electrodes that exhibited minimum error. For 1-DoF models using two retained electrodes (which mimics the current state of the art), subjects had average RMS errors of (depending on the DoF): 7.1-9.5% MVC for able-bodied and 13.7-17.1% MVC for limb-absent subjects. For 2-DoF models, subjects using four electrodes had errors on 1-DoF trials of 6.7-8.5% MVC for able-bodied and 11.9-14.0% MVC for limb-absent; and errors on 2-DoF trials of 9.9-11.2% MVC for able-bodied and 15.8-16.7% MVC for limb-absent subjects. For each model, retaining more electrodes did not statistically improve performance. The able-bodied results suggest that backward selection is a viable method for minimum error selection of as few as four electrode sites for these EMG-force tasks. Performance evaluation in a prosthesis control task is a necessary and logical next step for this site selection method.

A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control.

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.

The optimal controller delay for myoelectric prostheses.

A tradeoff exists when considering the delay created by multifunctional prosthesis controllers. Large controller delays maximize the amount of time available for EMG signal collection and analysis (and thus maximize classification accuracy); however, large delays also degrade prosthesis performance by decreasing the responsiveness of the prosthesis. To elucidate an "optimal controller delay" twenty able-bodied subjects performed the Box and Block Test using a device called PHABS (prosthetic hand for able bodied subjects). Tests were conducted with seven different levels of controller delay ranging from nearly 0-300 ms and with two different artificial hand speeds. Based on repeted measures ANOVA analysis and a linear mixed effects model, the optimal controller delay was found to range between approximately 100 ms for fast prehensors and 125 ms for slower prehensors. Furthermore, the linear mixed effects model shows that there is a linear degradation in performance with increasing delay.

The effects of static friction and backlash on extended physiological proprioception control of a powered prosthesis.

In general, externally powered prostheses do not provide proprioceptive feedback and thus require the user to rely on cognitively expensive visual feedback to effectively control the prosthesis. Applying the concept of extended physiological proprioception (EPP) to externally powered prostheses provides direct feedback to the user's proprioceptive system regarding the position, velocity, and forces applied to the prosthesis. However, electric elbows with EPP controllers developed at the Northwestern University Prosthetics Research Laboratory have exhibited unexplained "jerky" behavior in both clinical fittings and bench-top operation. In addition, the development of limit cycles, a specific type of constant-amplitude oscillation, had been observed in bench-top use of these elbows. Backlash and static friction within the EPP system were found to be primarily responsible for the development of limit cycles. Reducing static friction and backlash improved the system's performance. These results suggest that to most effectively implement EPP, prosthesis manufacturers should design prosthetic components that minimize static friction and backlash.