scholarly journals A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2943
Author(s):  
Linyi Zhang ◽  
Xi Chen ◽  
Pengfei Li ◽  
Chuang Wang ◽  
Mengxuan Li
Keyword(s):  
Human Body ◽  
Hand Movement ◽  
Electrode Array ◽  
Hand Movements ◽  
Human Hand ◽  
Human Computer Interactions ◽  
Sensing System ◽  
Electrostatic Induction ◽  
Planar Array ◽  
Measurement Algorithm

This paper proposes a method based on a planar array of electrostatic induction electrodes, which uses human body electrostatics to measure the height of hand movements. The human body is electrostatically charged for a variety of reasons. In the process of a hand movement, the change of a human body’s electric field is captured through the electrostatic sensors connected to the electrode array. A measurement algorithm for the height of hand movements is used to measure the height of hand movements after the direction of it has been obtained. Compared with the tridimensional array, the planar array has the advantages of less space and easy deployment; therefore, it is more widely used. In this paper, a human hand movement sensing system based on human body electrostatics was established to perform verification experiments. The results show that this method can measure the height of hand movements with good accuracy to meet the requirements of non-contact human-computer interactions.

An Integrated ARMA-Based Deep Autoencoder and GRU Classifier System for Enhanced Recognition of Daily Hand Activities

2020 ◽  
pp. 2152006
Author(s):  
Patricio Rivera ◽  
Edwin Valarezo ◽  
Tae-Seong Kim
Keyword(s):  
Daily Living ◽  
Recurrent Network ◽  
Hand Movements ◽  
Human Hand ◽  
Open Door ◽  
Wearable Sensor ◽  
Human Computer Interactions ◽  
Healthcare Applications ◽  
Classifier System ◽  
Gated Recurrent Unit

Recognition of hand activities of daily living (hand-ADL) is useful in the areas of human–computer interactions, lifelogging, and healthcare applications. However, developing a reliable human activity recognition (HAR) system for hand-ADL with only a single wearable sensor is still a challenge due to hand movements that are typically transient and sporadic. Approaches based on deep learning methodologies to reduce noise and extract relevant features directly from raw data are becoming more promising for implementing such HAR systems. In this work, we present an ARMA-based deep autoencoder and a deep recurrent network (RNN) using Gated Recurrent Unit (GRU) for recognition of hand-ADL using signals from a single IMU wearable sensor. The integrated ARMA-based autoencoder denoises raw time-series signals of hand activities, such that better representation of human hand activities can be made. Then, our deep RNN-GRU recognizes seven hand-ADL based upon the output of the autoencoder: namely, Open Door, Close Door, Open Refrigerator, Close Refrigerator, Open Drawer, Close Drawer, and Drink from Cup. The proposed methodology using RNN-GRU with autoencoder achieves a mean accuracy of 84.94% and F1-score of 83.05% outperforming conventional classifiers such as RNN-LSTM, BRNN-LSTM, CNN, and Hybrid-RNNs by 4–10% higher in both accuracy and F1-score. The experimental results also showed the use of the autoencoder improves both the accuracy and F1-score of each conventional classifier by 12.8% in RNN-LSTM, 4.37% in BRNN-LSTM, 15.45% CNN, 14.6% Hybrid RNN, and 12.4% for the proposed RNN-GRU.

Complex Unconstrained Three-Dimensional Hand Movement and Constant Equi-Affine Speed

2009 ◽  
Vol 101 (2) ◽  
pp. 1002-1015 ◽  
Author(s):  
Uri Maoz ◽  
Alain Berthoz ◽  
Tamar Flash
Keyword(s):  
Power Law ◽  
Hand Movement ◽  
Three Dimensional ◽  
Hand Movements ◽  
Human Hand ◽  
Local Geometry ◽  
Planning And Control ◽  
Human Movements ◽  
And Control ◽  
Geometry And Kinematics

One long-established simplifying principle behind the large repertoire and high versatility of human hand movements is the two-thirds power law—an empirical law stating a relationship between local geometry and kinematics of human hand trajectories during planar curved movements. It was further generalized not only to various types of human movements, but also to motion perception and prediction, although it was unsuccessful in explaining unconstrained three-dimensional (3D) movements. Recently, movement obeying the power law was proved to be equivalent to moving with constant planar equi-affine speed. Generalizing such motion to 3D space—i.e., to movement at constant spatial equi-affine speed—predicts the emergence of a new power law, whose utility for describing spatial scribbling movements we have previously demonstrated. In this empirical investigation of the new power law, subjects repetitively traced six different 3D geometrical shapes with their hand. We show that the 3D power law explains the data consistently better than both the two-thirds power law and an additional power law that was previously suggested for spatial hand movements. We also found small yet systematic modifications of the power-law's exponents across the various shapes, which further scrutiny suggested to be correlated with global geometric factors of the traced shape. Nevertheless, averaging over all subjects and shapes, the power-law exponents are generally in accordance with constant spatial equi-affine speed. Taken together, our findings provide evidence for the potential role of non-Euclidean geometry in motion planning and control. Moreover, these results seem to imply a relationship between geometry and kinematics that is more complex than the simple local one stipulated by the two-thirds power law and similar models.

Analysis and Systematic Classification of Human Hand Movement for Robot Hand Design

2008 ◽  
Vol 20 (3) ◽  
pp. 429-435 ◽  
Author(s):  
Takeshi Ninomiya ◽  
◽  
Takashi Maeno ◽  
Keyword(s):  
Hand Movement ◽  
Hand Movements ◽  
Human Hand ◽  
Robot Hand ◽  
Systematic Classification ◽  
Robot Hands ◽  
Human Muscles ◽  
Set Up ◽  
Human Hands

The systematic classification of hand movements, which indicates the minimum mechanism of robot hands, is suggested. The performance of existent robot hands is not as high as that of human hands because the performance of existent actuators does not come up to that of human muscles in the same volume. It is important for robot hands to accomplish targeted tasks with a minimum mechanism. Human hand movements are analyzed quantitatively considering robot hands such as associated movement of DIP and PIP joints. Based on the results of analysis, we obtain three items, i.e., fingers, joints that must be set up actuators and basic movements we define. We systematically classify human hand movement for the robot hand based on three items.

scholarly journals Kinetic Energy Harvesting from Human Hand Movement by Mounting micro Electromagnetic Generator

2019 ◽  
Vol 115 ◽  
pp. 02005
Author(s):  
F. R. Pathan
Keyword(s):  
Energy Harvesting ◽  
Kinetic Energy ◽  
Thermal Energy ◽  
Human Body ◽  
Mechanical Energy ◽  
Hand Movement ◽  
The Body ◽  
Human Hand ◽  
Electromagnetic Generator ◽  
Static Energy

A comprehensive review of design and experimentation is presented in this research paper on sustainable renewable energy scavenging from Human body movement using Micro electromagnetic kinetic energy harvester to powering wearable, portable electronics, implantable medical devices etc. The body location which is chosen as the harvester is human hand between elbow and shoulder. Human body harvest energy in two ways i,e, mechanical energy and thermal energy. Mechanical energy is of two kinds one is static energy and the other one is kinetic energy. Due to motion or displacement or enforcement excitation the kinetic energy is extracted. The electric charges which remains imbalance on the surface or within a material is static energy. Thermal energy is extracted from the dissipation of heat from human body. Human body parts and organs generate energy through two types of activities are voluntary and involuntary. The energy which are produced by voluntary activities are high as people intentionally does work by body motion, walk, run. The generated energy by involuntary organs like heart, breathing, artery are smaller compare to voluntary energy harvesting. One process of energy harvesting is by use of micro electromagnetic generator, flexible and stretchable piezoelectric, triboelectric, electromagnetic induction, PVDF cantilever mounting on human body. The harvester prototype is cylindrical magnet L40xD10 mm size which is mounted on human hand for energy harvesting. While in movement of hand the produced wave forms by magnetic generator are measured and recorded for calculation. Analyzing the received data it has been found that the generated power by micro electromagnetic vibration generator from movement of human hand are 319 RMS μW and 2.48 RMS mV with a frequency of 0.25 Hz and power density of about 2.48μW/cm³.

scholarly journals Research on Hand Action Pattern Recognition of Bionic Limb Based on Surface Electromyography

2021 ◽  
Vol 271 ◽  
pp. 01030
Author(s):  
Zihan Yin
Keyword(s):  
Pattern Recognition ◽  
Human Body ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Main Tool ◽  
Human Movement ◽  
Human Hand ◽  
Communication Tool ◽  
Action Pattern ◽  
Hand Action

Hands are important parts of a human body. It is not only the main tool for people to engage in productive labor, but also an important communication tool. When the hand moves, the human body produces a kind of signal named surface electromyography (sEMG), which is a kind of electrophysiological signal that accompanies muscle activity. It contains a lot of information about human movement consciousness. The bionic limb is driven by multi-degree-freedom control, which is got by converting the recognition result and this can meet the urgent need of people with disabilities for autonomous operation. A profound study of hand action pattern technology based on sEMG signals can achieve the ability of the bionic limb to distinguish the hand action fast and accurately. From the perspective of the pattern recognition of the bionic limb, this paper discussed the human hand action pattern recognition technology of sEMG. By analyzing and summarizing the current development of human hand movement recognition, the author proposed a bionic limb schema based on artificial neural network and the improved DT-SVM hand action recognition system. According to the research results, it is necessary to expand the type and total amount of hand movements and gesture recognition, in order to adapt to the objective requirements of the diversity of hand action patterns in the application of the bionic limb.

scholarly journals 1-DOF Finger Mechanism Design for Anthropomorphic Prosthetic Hand with Similarity Criteria for Human Hand Finger Movement

2019 ◽  
Vol 19 (2) ◽  
pp. 97
Author(s):  
Romdhoni Nur Huda ◽  
Lobes Herdiman ◽  
Taufiq Rochman
Keyword(s):  
Hand Movement ◽  
Similarity Criteria ◽  
Hand Movements ◽  
Human Hand ◽  
Objective Functions ◽  
Prosthetic Design ◽  
Standard Size ◽  
Optimization Criteria ◽  
Prosthetic Hands ◽  
Prosthetic Finger

Development of prosthetic hands continues to be made to get prosthetic which has special characteristics, namely anthropomorphism. The anthropomorphic prosthetic design refers to the improvement and development of the design to the stage of the similarity of the prosthetic movement to human hand movements. This study carries the design of the anthropomorphic 1-DOF prosthetic finger mechanism to get prosthetic at an affordable price. The optimization criteria for the similarity of movement with the human hand are formulated with two objective functions, namely the similarity of the range of motion and the total length of the finger that is completed simultaneously. The human hand movement that is a reference is the movement of conical object grasping on a standard size conical according to the maximum hand-held diameter anthropometry of an Indonesian people.

Context-dependent relationship in high-resolution micro-ECoG studies during finger movements

2020 ◽  
Vol 132 (5) ◽  
pp. 1358-1366
Author(s):  
Chao-Hung Kuo ◽  
Timothy M. Blakely ◽  
Jeremiah D. Wander ◽  
Devapratim Sarma ◽  
Jing Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Sensorimotor Cortex ◽  
Index Finger ◽  
Hand Movement ◽  
Cortical Activation ◽  
Hand Movements ◽  
Finger Movements ◽  
Thumb Movement ◽  
Neurosurgical Patients ◽  
The Relationship

OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

Hand Movements and Nondominant Fluency in Bilinguals

1979 ◽  
Vol 48 (1) ◽  
pp. 207-214 ◽  
Author(s):  
Luis R. Marcos
Keyword(s):  
Cognitive Processes ◽  
Motor Behavior ◽  
Hand Movement ◽  
Movement Behavior ◽  
Hand Movements ◽  
Language Fluency ◽  
Movement Activity

16 subordinate bilingual subjects produced 5-min. monologues in their nondominant languages, i.e., English or Spanish. Hand-movement activity manifested during the videotape monologues was scored and related to measures of fluency in the nondominant language. The hand-movement behavior categorized as Groping Movement was significantly related to all of the nondominant-language fluency measures. These correlations support the assumption that Groping Movement may have a function in the process of verbal encoding. The results are discussed in terms of the possibility of monitoring central cognitive processes through the study of “visible” motor behavior.

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