The Precision of the Human Hand: Variability in Pinch Strength and Manual Dexterity

Changes in hand morphology throughout human evolution have facilitated the use of forceful pad-to-pad precision grips, contributing to the development of fine motor movement and dexterous manipulation typical of modern humans. Today, variation in human hand function may be affected by demographic and/or lifestyle factors, but these remain largely unexplored. We measured pinch grip strength and dexterity in a heterogeneous cross-sectional sample of human participants (n = 556) to test for the potential effects of sex, age, hand asymmetries, hand morphology, and frequently practiced manual activities across the lifespan. We found a significant effect of sex on pinch strength, dexterity, and different directional asymmetries, with the practice of manual musical instruments, significantly increasing female dexterity for both hands. Males and females with wider hands were also stronger, but not more precise, than those with longer hands, while the thumb-index ratio had no effect. Hand dominance asymmetry further had a significant effect on dexterity but not on pinch strength. These results indicate that different patterns of hand asymmetries and hand function are influenced in part by life experiences, improving our understanding of the link between hand form and function and offering a referential context for interpreting the evolution of human dexterity.

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

Objective: Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach: We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its outer layers with only 12-minute individual-related data. Main results: The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%. Significance: The proposed methods could foster future BCI applications relying on few-channel portable recording devices and individual-based training.

Inter- and Intra-racial Differences in Pain and Performance-Based Function in Older Adults With Osteoarthritis

Osteoarthritis (OA) contributes to movement-evoked pain, impaired function and mobility, and reduced quality of life among older adults. Assessment of pain has not traditionally considered the dynamic changes that occur with gross motor movement, and thus self-reports of pain often reflect static or resting pain. This case-control pilot study examined inter- and intra-racial differences in movement-evoked pain and performance-based function in older adults (N= 28) with knee OA. Cases consisted of Blacks and Whites with OA; controls included Blacks without OA. The Biodex Pro System 4, an isokinetic and isometric dynamometer commonly used in rehabilitative medicine, measured knee muscle function. Pain intensity was assessed pre-, ante-, and post- completion of 2 repetition sets of five alternating knee flexion and extension maximum voluntary contractions at angular velocities of 90° (greater resistance) and 180° (lower resistance). Repeated Measures Analysis of Variance with Bonferroni correction identified statistically significant differences in pain for within- and between-subjects at 90° and 180°. Pain increased during the repetitions and decreased after completion of both repetition sets; this non-linear relationship was significant (p= .004). One-way ANOVA demonstrated peak torque (extension), a muscle’s maximum strength capability, was significantly higher in White cases and Blacks controls compared to Blacks cases. Novel findings revealed that baseline pain is much higher and functional performance is significantly lower in Blacks with OA compared to White cases and Black controls. This research advances precision pain measurement and our understanding of the biological mechanisms uniquely involved in the experience of knee OA and mobility.

Motor Preparation and Execution for Performance Difficulty: Centroparietal Beta Activation during the Effort Expenditure for Rewards Task as a Function of Motivation

Debate exists as to the effects of anxiety in performance-based studies. However, no studies have examined the influence of motivation both in preparation of a motor movement and during movement performance. The present study measured beta activation in preparation for and during execution of the effort expenditure for rewards task (EEfRT), a button-pressing task consisting of easy and hard trials. Results indicated that motor preparation (i.e., reduced beta activation) was greater in preparation for hard trials than for easy trials. Additionally, motor preparation decreased (i.e., beta activation increased) over the course of hard trial execution. These results suggest that motor preparation is enhanced prior to more challenging tasks but that motor preparation declines as participants become closer to completing their goal in each challenging trial. These results provide insight into how beta activation facilitates effort expenditure for motor tasks varying in difficulty and motivation. The impact of these results on models of anxiety and performance is discussed.

Konsep Pembelajaran Menyenangkan bagi Siswa Kelas Bawah Tingkat Sekolah Dasar

Fun learning is a method of learning that uses a basic needs-based approach for lower-class students. Fun learning is needed especially for lower-class students who have characteristics: a lot of moving, requiring a lot of teacher attention and a lot of introducing new things for students with a tendency to a limited level of concentration of lower grade students. This fun learning is an urgent need in this modern era. Therefore, this study aims to explain the concept of fun learning, especially for elementary school students (grades 1-3) elementary school level. The methodology used in this research uses a type of qualitative research with an analytical descriptive approach. The results showed that the concept of learning fun for elementary school lower class students should be based on the needs of lower-class students who prioritize motor movement over cognitive, explain the material in a visual way rather than auditory, involve students in the learning process (two-way learning rather than one-way), and include elements of games in learning.

Dual-Task-Based Drum Playing with Rhythmic Cueing on Motor and Attention Control in Patients with Parkinson’s Disease: A Preliminary Randomized Study

Although there have been increasing reports regarding the effectiveness of dual-task interventions in rehabilitation, the scope of this research is limited to gross motor movement, such as gait among patients with Parkinson’s disease (PD). To expand the dual-task paradigm to upper extremity motor and attention control in PD, drum playing with modulation of musical elements was attempted. The objective of this study was to evaluate the effects of a drum playing intervention with rhythmic cueing on upper extremity motor control and attention control in patients with PD. Twelve participants were randomly assigned to the drum playing intervention with rhythmic cueing group or the control group. The results showed that the drum playing with rhythmic cueing (DPRC) group significantly increased their sustained time of entrainment (45 BPM) and their latency time until entrainment from pretest to posttest. For the DPRC group, the latency time until entrainment was significantly improved, and improvements in cognitive measures were also found. This study shows that DPRC has great potential to improve upper extremity motor control and attention control and supports the development of new interventions that include this technique for rehabilitation in patients with PD.

The Validity and Reliability Study of the Basic Motor Movement, Social Skill Observation, and Evaluation Scale for Basic Movement Education

It is aimed to develop a measurement tool which is a basic motor movement, social skill, attitude observation and evaluation scale for Basic Movement Education. In the study, the natural observation from qualitative research methods is used. The scale outline with 40 items is formed as a result of literature review and negotiations. To determine the content validity of the scale draft, the opinions of six field experts (lecturers), two preschool teachers, two physical education (PE) teachers, and two language experts were analyzed using the Lashwe technique. By analyzing the experts’ opinions according to Lashwe technique, four items are removed since their content validity rate (CVR) assets are less than 0.75. CVR shows that the scale provides 4-point Likert-type construct validity and consists of two subdimensions with 36 items (the basic motor movement skill dimension is 16 items and social skill dimension is 20 items). Total points of the Spearman correlation coefficient are checked among the observers. While the correlation coefficient of the basic motor movement skill dimension is .71 ( p = .000), the correlation coefficient of social skill dimension is .82 ( p = .002). The correlation coefficient among the total points is more than .70, and the results ( p < .01) are meaningful. This shows that the measurement tool is reliable, and the scale provides reliability with 4-point Likert-type gradation.

Integrating Step Motor Movement Half-Step and Full-Step by Labview

This paper represents the performance of placement control of step motor (Brushless DC electric motor) using LabVIEW. Step motors are Applicable in many industries and factories where accurate control of motor position is necessary. In this work, half step and full step manner of step motor control is implemented. The experiment outcome show that half step manner makes an angle of 0.6 degree per step (300 steps), considering full step makes 1.2 degree per step (150 steps). With LabVIEW the arrangement has friendly communication, better control result and uncomplicated control in both ways.

Repetitive throat clearing, blinking, and grimacing

Tourette’s disorder requires the presence of multiple motor tics and at least one vocal tic that have been present for at least one year. A tic is a sudden, rapid, recurrent, nonrhythmic motor movement or vocalization. Tics are often associated with a premonitory urge and may be temporarily suppressed. They typically begin between the age of four and six years, peak in severity in early adolescence, and then diminish. The majority of individuals spontaneously achieve remission by early adulthood. Because tic disorders commonly co-occur with psychiatric disorders, they should be screened for in every psychiatric interview. Treatment should begin with psychoeducation and focus on symptom reduction rather than remission. Behavioral interventions, including habit reversal therapy, may be considered. Medications, such as antipsychotics and alpha2 adrenergic agonists, should be considered if tics cause severe impairment and are moderate to severe.

A deep descriptor for cross-tasking EEG-based recognition

Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.