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Hand tremor is one of the dominating symptoms of Parkinson’s disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

The aim of this study is to review our longitudinal experience with onabotulinumtoxinA (onaBoNT-A) injections for medically refractory hand tremor. We performed a retrospective review of our database of patients treated with onaBoNT-A for hand tremor evaluated between 2010 and 2018 in at least 2 sessions with follow-up. The majority were injected into the forearm flexors (FF), although treatment was individualized. During the specified period, 91 patients (53 essential tremor, 31 dystonic tremor, 6 Parkinson’s disease tremor, and 1 cerebellar outflow tremor) met our inclusion criteria. The mean age (SD) was 64.8 years (12.8), and mean duration of follow-up was 29.6 months (25.1) with mean of 7.7 (6.3) treatment visits. FF were injected in 89 (97.8%) patients, exclusively in 74 (81.3%), and 15 (16.5%) were injected in FF and other muscles. EMG guidance was used in 5 patients (5.5%). On a 0–4 “peak effect” rating scale (0 = no effect, 4 = marked improvement in severity and function), 80.2% and 85.7% of patients reported moderate or marked improvement (score 3 or 4) at their first and last follow-up visit, respectively. There was no statistically significant difference in the outcomes between first and last visit: average “peak effect” rating score (3.2 versus 3.4), “global” rating score (3.0 versus 3.2), latency of response (4.5 versus 3.8 days), and total duration of response (12.7 versus 12.8 weeks), except onaBoNT-A dose (65.0 versus 78.6 U/limb, p = 0.002). Of 1095 limb injections, there were 134 (12.2%) non-disabling and transient (mean 36 days) adverse events (132 limb weakness, 2 pain). OnaBoNT-A injections are safe and effective in the treatment of hand tremor.

Background: Transcutaneous afferent patterned stimulation (TAPS) is a wrist-worn, non-invasive therapy delivering calibrated stimulation to the median and radial nerves. While the efficacy and safety of TAPS therapy for essential tremor (ET) have been demonstrated, current evidence supporting therapeutic benefits of TAPS versus standard of care (SOC) is lacking. This prospective study evaluated the clinical benefit of adding TAPS treatment to SOC versus SOC alone. Methods: This randomized, controlled, pragmatic trial recruited patients from a large health plan’s Commercially Insured and Medicare Advantage population. 310 patients were randomized 1:1 to one month of treatment with TAPS (TX) or standard of care (SOC). The pre-specified endpoints were changes in tremor power measured by motion sensors (primary) and improvement in Bain & Findley Activities of Daily Living (BF-ADL) upper limb scores reported (secondary) between TX and SOC at one month. Results: 276 patients completed the one-month endpoints (n=133 TX, n=143 SOC). The study met the primary and secondary endpoints, with significantly reduced tremor power in TX than SOC (0.017 (0.003) versus 0.08 (0.014) (m/s2)2; geometric mean (SE); p < 0.0001) and greater improvement in the BF-ADL score in TX than SOC (1.6 (0.43) vs 0.2 (0.37) points; mean (SE); p < 0.05). 82% of TX patients experienced tremor improvement from before to after therapy. No serious device-related adverse events were reported. Discussion: This trial demonstrates that TAPS significantly improves tremor power and BF-ADLs in patients with ET compared to SOC over one month of home use. Highlights This study found that TAPS significantly improves tremor power and BF-ADL scores in patients with ET compared to SOC over one month of home use. This real-world study suggests that non-invasive TAPS therapy is a safe and valuable treatment option for patients with ET.

With the increasing prevalence of neurodegenerative diseases, including Parkinson’s disease, hand tremor detection has become a popular research topic because it helps with the diagnosis and tracking of disease progression. Conventional hand tremor detection algorithms involved wearable sensors. A non-invasive hand tremor detection algorithm using videos as input is desirable but the existing video-based algorithms are sensitive to environmental conditions. An algorithm, with the capability of detecting hand tremor from videos with a cluttered background, would allow the videos recorded in a non-research environment to be used. Clinicians and researchers could use videos collected from patients and participants in their own home environment or standard clinical settings. Neural network based machine learning architectures provide high accuracy classification results in related fields including hand gesture recognition and body movement detection systems. We thus investigated the accuracy of advanced neural network architectures to automatically detect hand tremor in videos with a cluttered background. We examined configurations with different sets of features and neural network based classification models. We compared the performance of different combinations of features and classification models and then selected the combination which provided the highest accuracy of hand tremor detection. We used cross validation to test the accuracy of the trained model predictions. The highest classification accuracy for automatically detecting tremor (vs non tremor) was 80.6% and this was obtained using Convolutional Neural Network-Long Short-Term Memory and features based on measures of frequency and amplitude change.

This article is about vibration-damping robotic eating devices designed for use by people who have difficulty in eating due to hand tremors due to neuromuscular system disorder. The robotic eating device has two degrees of freedom (DoF). It contains an active controller structure to absorb vibrations in the y- and z-directions. In the handle part of the robotic eating device, there are two DC motors placed on the y- and z-axis, a three-axis IMU inertia sensor, an embedded system board, and a power unit. To absorb the vibration measured from the IMU sensor, the position control of the two motors to which the spoon is connected is provided by PID controllers. The part of the spoon (the pit surface) where the food is placed is tried to be kept constant. To test the vibration-damping performance of the control method, the dynamic model of the spoon along the eating kinematic trajectory was simulated in the SimMechanics environment using vibration data from ten tremor patients. The results show that the stabilization method can absorb the vibration in the hand of the person in the range of 84–99.409% and successfully provide the stabilization of the spoon tip. This damping rate is promising for providing a healthy diet for hand tremor patients.

Hand tremor typically has a negative impact on a person's ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the \"before\" and \"after\" medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.

The accuracy level is important in shooting activities and depends on many factors, such as hand tremors as body vibration and shooting position. Achieving high accuracy in different shooters is challenging, especially in the case of different shooting positions. However, there is a lack of information about the influence of shooting positions and experiences on a shooter’s body vibration and accuracy levels. Thus, this study aims to develop a portable and low-cost hand tremor measurement device (as a function of body vibration) to identify the influence of hand movement on shooting accuracy. For this purpose, low-cost accelerometer sensors and a microcontroller were used as the measurement kit. Three different shooting positions (squatting, standing, and prone) were analyzed. The shooters were classified into novice and expert groups. Each group had five participants with standard fire guns and accelerometer kits. These participants were asked to shoot the target to get their best accuracy. Besides, the hand tremor level data from the self-developed kit were recorded to investigate the hand tremors. The results show that the novice participants have more hand tremors in all shooting positions. There are significant differences between the squatting, standing, and prone positions in hand tremors for novice and expert participants. In the expert group, the prone and squatting positions have the least vibration level, indicated by the least acceleration (0.01 - 0.04 m/s2 for the expert group and 0.02 - 0.11 m/s2 for the novice group). The best accuracy for all positions is also obtained from expert shooters. It can be concluded that different shooting positions are related to the body vibrations. The expert shooters have a lower body vibration than the novice participants. The hand tremor levels may influence the accuracy level since different shooting positions and experiences have different vibration and accuracy levels

PurposeDuring 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.MethodsIn 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.ResultsHand 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.ConclusionThese 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.

Purpose To monitor the progression of diseases such as Parkinson’s disease (PD) or essential tremor (ET), there is a growing interest in understanding their side effects and continuously monitoring the deterioration or progress of patients’ health conditions. The objective of this study was to investigate the feasibility of a wearable monitoring device constructed from compact MEMS for robust tremor detection in the upper limb using three different storage and monitoring techniques. Method Four subjects (2 PD and 2 ET) with varying stages of disease and treatment willingly provided offline, online, and live modes of tremor data using a low-cost, miniaturized accelerometer and microelectromechanical device. Results The results demonstrated differences in voluntary and non-voluntary characteristics of various activities and the distinct separation between them in the vibration spectrum at the limit of 2 Hz. Online and live monitoring provided the best alternatives to continuous in-home tracking combined with extensive post-processing techniques to detect tremor segments. The findings also highlighted the emergence of noticeable peaks, in the range between 3 and 8 Hz, for the PD’s frequency response, compared to the ET case, where the broadband behavior dominates. Conclusion The possibility of using a dynamic tuned mass damper tuned with dominant peaks to be canceled opens opportunities for PD passive tremor suppression.

Background: Mild hand tremor occurs in most normal adults. There are no surveys of the prevalence or clinical correlates of such tremor among children. Methods: A cross-sectional study of tics, tremor and other neurological disorders was conducted in Spanish children; thus, 819 schoolchildren in Burgos, Spain, drew Archimedes spirals with each hand. Tremor in spirals was rated (0–2) by a blinded neurologist and an overall tremor rating (0–4) was assigned. Results: The mean age was 10.9 ± 3.1 years. A tremor rating of 1 (mild tremor) was present in either hand in 424 (51.7%) children, and in both hands in 88 (10.7%) children. Higher tremor ratings were very uncommon. The overall tremor rating was higher in boys than girls (1.31 ± 0.41 vs. 1.22 ± 0.34, p = 0.002) and correlated weakly yet significantly with age (ρ = 0.09, p = 0.01). Within subjects, the left hand spiral rating was greater than the right (p < 0.001). Conclusions: In this cross-sectional study of 819 Spanish schoolchildren, mild tremor was commonly observed. As in adults, males had more tremor than females, tremor scores increased with age, and tremor scores were higher in the left than right arm, demonstrating that these clinical correlations seem to be more broadly generalizable to children. The functional significance of tremor in children, particularly as it relates to handwriting proficiency, deserves additional scrutiny.