UNASSIGNED: Hand postural physiological tremor increases during arithmetic computation. The present work aims at investigating whether this could be attributed to a raise in stress for having to provide a correct answer within a constrained period of time, or to voice vibration for having to speak to report the answer.
UNASSIGNED: In 16 participants tremor was recorded by using a 3-axial accelerometer during 5 min of a hand postural task performed simultaneously while: 1) subtracting 13 from a 3-digit number within 4 s and with mistakes correction (intervention: math stress task), 2) same as for the \"intervention task\" but without time limit and mistakes correction (intervention: math nonstress task), 3) adding 1 to a 3-digit number (intervention: voice vibration task), and 4) only postural task while keeping quiet (control task). Electromyographic (EMG) activity from the extensor carpi radialis was measured during the hand postural task.
UNASSIGNED: Compared to control, tremor increased during both math interventions (+30.9 % p = 0.002, math stress; +15.0 % p = 0.01, math nonstress), but not during the voice vibration task (+12.2 % p = 0.239). During the math stress trial tremor was greater compared to both the voice vibration trial (+21.0 % p = 0.021), and the math nonstress trial (+13.5 % p = 0.01). EMG activity was not affected.
UNASSIGNED: The results suggest that during arithmetic computation the \"stress component\" contributes only partially to the observed increase in hand postural tremor, and that this increase cannot be attributed to voice vibrations.

Physiologic hand tremors are a critical factor affecting the aim of air pistol shooters. However, the extent of the effect of hand tremors on shooting performance is unclear. In this study, we aim to explore the relationship between hand tremors and shooting performance scores as well as investigate potential links between muscle activation and hand tremors. In this study, 17 male air pistol shooters from China\'s national team and the Air Pistol Sports Center were divided into two groups: the elite group and the sub-elite group. Each participant completed 40 shots during the experiment, with shooters\' hand tremors recorded using three-axis digital accelerometers affixed to their right hands. Muscle activation was recorded using surface electromyography on the right anterior deltoid, posterior deltoid, biceps brachii (short head), triceps brachii (long head), flexor carpi radialis, and extensor carpi radialis. Our analysis revealed weak correlations between shooting scores and hand tremor amplitude in multiple directions (middle-lateral, ML: r2 = -0.22, p < 0.001; vertical, VT: r2 = -0.25, p < 0.001), as well as between shooting scores and hand tremor complexity (ML: r2 = -0.26, p < 0.001; VT: r2 = -0.28, p < 0.001), across all participants. Notably, weak correlations between shooting scores and hand tremor amplitude (ML: r2 = -0.27, p < 0.001; VT: r2 = -0.33, p < 0.001) and complexity (ML: r2 = -0.31, p < 0.001) were observed in the elite group but not in the sub-elite group. Moderate correlation were found between the biceps brachii (short head) RMS and hand tremor amplitude in the VT and ML directions (ML: r2 = 0.49, p = 0.010; VT: r2 = 0.44, p = 0.025) in all shooters, with a moderate correlation in the ML direction in elite shooters (ML: r2 = 0.49, p = 0.034). Our results suggest that hand tremors in air pistol shooters are associated with the skill of the shooters, and muscle activation of the biceps brachii (long head) might be a factor affecting hand tremors. By balancing the agonist and antagonist muscles of the shoulder joint, shooters might potentially reduce hand tremors and improve their shooting scores.

In the absence of disease, humans produce smooth and accurate movement trajectories. Despite such \'macroscopic\' aspect, the \'microscopic\' structure of movements reveals recurrent (quasi-rhythmic) discontinuities. To date, it is unclear how the sensorimotor system contributes to the macroscopic and microscopic architecture of movement. Here, we investigated how corticospinal excitability changes in relation to microscopic fluctuations that are naturally embedded within larger macroscopic variations in motor output. Participants performed a visuomotor tracking task. In addition to the 0.25 Hz modulation that is required for task fulfilment (macroscopic scale), the motor output shows tiny but systematic fluctuations at ∼2 and 8 Hz (microscopic scales). We show that motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) during task performance are consistently modulated at all (time) scales. Surprisingly, MEP modulation covers a similar range at both micro- and macroscopic scales, even though the motor output differs by several orders of magnitude. Thus, corticospinal excitability finely maps the multiscale temporal patterning of the motor output, but it does so according to a principle of scale invariance. These results suggest that corticospinal excitability indexes a relatively abstract level of movement encoding that may reflect the hierarchical organisation of sensorimotor processes. KEY POINTS: Motor behaviour is organised on multiple (time)scales. Small but systematic (\'microscopic\') fluctuations are engrained in larger and slower (\'macroscopic\') variations in motor output, which are instrumental in deploying the desired motor plan. Corticospinal excitability is modulated in relation to motor fluctuations on both macroscopic and microscopic (time)scales. Corticospinal excitability obeys a principle of scale invariance, that is, it is modulated similarly at all (time)scales, possibly reflecting hierarchical mechanisms that optimise motor encoding.

OBJECTIVE: During a cognitive effort, an increase in cortical electrical activity, functional alterations in the anterior cingulate cortex, and modifications in cortical inputs to the active motor units have been reported. In light of this, an increase in tremor could be anticipated as result of a mental task. In the present work, we tested this hypothesis.
METHODS: In 25 individuals, tremor was measured with a three-axial accelerometer during 300 s of postural and goal-directed tasks performed simultaneously to mental calculation, or during control (same tasks without mental calculation). Hand and finger dexterity were also evaluated. Electromyographic (EMG) recordings from the extensor digitorum communis were collected during the postural task.
RESULTS: Hand and finger dexterity was negatively affected by the mental task (p = .003 and p = .00005 respectively). During mental calculation, muscle tremor increased in the hand postural (+ 29%, p = .00005) but not in the goal-directed task (- 1.5%, p > .05). The amplitude of the main frequency peak also increased exclusively in the hand postural task (p = .028), whilst no shift in the position of the main frequency peak was observed. EMG was not affected.
CONCLUSIONS: These results support the position of the contribution of a central component in the origin of physiological hand postural tremor. It is suggested that the different effect of mental calculation on hand postural and goal-directed tasks can be attributed to the different origins and characteristics of hand postural and goal-directed physiological tremor.

Teleoperation robot systems can help humans perform tasks in unstructured environments. However, non-intuitive control interfaces using only a keyboard or joystick and physiological tremor reduce the performance of teleoperation. This paper presents an intuitive control interface based on the wearable device gForcePro+ armband. Two gForcePro+ armbands are worn at the centroid of the upper arm and forearm, respectively. Firstly, the kinematics model of the human arm is established, and the inertial measurement units (IMUs) are used to capture the position and orientation information of the end of the arm. Then, a regression model of angular transformation is developed for the phenomenon that the rotation axis of the torsion joint is not perfectly aligned with the limb segment during motion, which can be applied to different individuals. Finally, to attenuate the physiological tremor, a variable gain extended Kalman filter (EKF) fusing sEMG signals is developed. The described control interface shows good attitude estimation accuracy compared to the VICON optical capture system, with an average angular RMSE of 4.837° ± 1.433°. The performance of the described filtering method is tested using the xMate3 Pro robot, and the results show it can improve the tracking performance of the robot and reduce the tremor.

BACKGROUND: Continuous curvilinear capsulorhexis (CCC) is a delicate ophthalmic procedure which may benefit from robot technology. Measuring the behaviours (physiological tremor, operation force) of surgeons provides baseline data to develop assistive CCC robot.
METHODS: A forceps with fibre bragg grating and inertial sensors is used to measure the surgeons\' behaviours while experts/novices perform CCC on ex-vivo pig eyes, in-vivo rabbit eyes and ex-vivo human lens.
RESULTS: In pig/rabbit tests, the root-mean-square (RMS) tremor amplitude is 35.26/59.04 μm (expert/novice, transverse), 13.3/20.55 μm (axial). The RMS voluntary force (VF) and involuntary force (IF) are 8.97/17.16 mN, and 0.66/1.90 mN, respectively. In human lens test, the RMS tremor amplitude is 24.0 μm (transverse, expert only), 9.88 μm (axial). The RMS VF and RMS IF are 9.04 mN (expert only) and 0.17 mN, respectively.
CONCLUSIONS: The expert surgeons have better precision and less operation force.

OBJECTIVE: This study aimed to compare the physiological tremor, grip strength, and cognitive function of sedentary and physically active older adults.
METHODS: Twenty-four older adults aged ≥65 years participated in this study and were divided into the sedentary (76.5±4.4 years, n=12) and physically active (73.5±3.3 years, n=12) groups. Each group completed the Mini-Mental State Examination (MMSE) for cognitive function assessment. Physiological tremor was measured using an accelerometer for both hands at rest and the left/right hand with a 1,000 g dumbbell on the palm in neutral positions and the elbow flexed at 90°. Physical fitness was measured by grip strength and completion of the Short Physical Performance Battery (SPPB) and the 6-min walk test.
RESULTS: The physically active group showed a significantly lower level of physiological tremor in both hands at rest and the left/right hand with a 1,000 g dumbbell on the palm (P<0.05) than that in the sedentary group. For cognitive function, the physically active group showed significantly higher scores than those in the sedentary group (P<0.001). No significant correlation was found between cognitive function and left/right grip strength (left: r = 0.117, P = 0.585; right: r = 0.230, P = 0.279), physiological tremor in both hands at rest (left: r = -0.524, P < 0.001; right: r = -0.508, P < 0.05), and the left/right hand with a 1,000 g dumbbell on the palm (left: r = -0.505, P < 0.05; right: r = -0.458, P < 0.05).
CONCLUSIONS: Physiological tremor of the hands has the potential to be a useful predictor of cognitive function in older adults.

Fatigue is defined as \"a loss of force-generating capacity\" in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant\'s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier\'s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.

OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools.
METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson\'s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125-9.375 Hz) and band 11 (B11: 15.625-17.1875 Hz).
RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals.
CONCLUSIONS: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

OBJECTIVE: Assessment of physiological tremor during neurosurgical procedures may provide further insights into the composites of surgical expertise. Virtual reality platforms may provide a mechanism for the quantitative assessment of physiological tremor. In this study, a virtual reality simulator providing haptic feedback was used to study physiological tremor in a simulated tumor resection task with participants from a \"skilled\" group and a \"novice\" group.
METHODS: The task involved using a virtual ultrasonic aspirator to remove a series of virtual brain tumors with different visual and tactile characteristics without causing injury to surrounding tissue. Power spectral density analysis was employed to quantitate hand tremor during tumor resection. Statistical t test was used to determine tremor differences between the skilled and novice groups obtained from the instrument tip x, y, z coordinates, the instrument roll, pitch, yaw angles, and the instrument haptic force applied during tumor resection.
METHODS: The study was conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
METHODS: The skilled group comprised 23 neurosurgeons and senior residents and the novice group comprised 92 junior residents and medical students.
RESULTS: The spectral analysis allowed quantitation of physiological tremor during virtual reality tumor resection. The skilled group displayed smaller physiological tremor than the novice group in all cases. In 3 out of 7 cases the difference was statistically significant.
CONCLUSIONS: The first investigation of the application of a virtual reality platform is presented for the quantitation of physiological tremor during a virtual reality tumor resection task. The goal of introducing such methodology to assess tremor is to highlight its potential educational application in neurosurgical resident training and in helping to further define the psychomotor skill set of surgeons.