Left Hand and Right Hand Throwing Mechanism Patterns Classification

2013 ◽  
Vol 61 (2) ◽  
Author(s):  
Ching Yee Yong ◽  
Rubita Sudirman ◽  
Nasrul Humaimi Mahmood ◽  
Kim Mey Chew
Keyword(s):  
Hand Movement ◽  
Human Motion ◽  
Graphical Form ◽  
Human Hand ◽  
Shoulder Injury ◽  
Hand Motion ◽  
Right Hand ◽  
Left And Right ◽  
2D And 3D

This study investigates and acts as a trial clinical outcome for human hand motion and behaviour analysis. It was analysed and accessed the quality of human motion that can be used to differentiate the left and right hand throwing action patterns and also the effect of throwing distance to shoulder pain. It aims to establish how widespread the quality of life effects of human motion especially hands movement. Gyroscope, accelerometer and compass sensors were used to measure the hand movement for a throwing process. 2D and 3D scatter plotting were proposed to represent data in graphical form. An experiment was set up in a laboratory environment with conjunction of analysing human motion. The instruments demonstrate 2D and 3D scatter plot enable distinguish left and right hand throwing action patterns significantly. Distribution of gyroscope data shows that a throwing mechanism for a greater distance may bring greater probability of shoulder injury.

Human Hand Movement Analysis Using Principle Component Analysis Classifier

2013 ◽  
Vol 284-287 ◽  
pp. 3126-3130 ◽  
Author(s):  
Ching Yee Yong ◽  
Rubita Sudirman ◽  
Nasrul Humaimi Mahmood ◽  
Kim Mey Chew
Keyword(s):  
Hand Movement ◽  
Arm Movement ◽  
Movement Analysis ◽  
Human Motion ◽  
Human Hand ◽  
Human Arm ◽  
Set Up ◽  
And Behavior ◽  
Adequate Internal Consistency

This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. It was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum scatter plot in gyroscope and accelerometer for pattern classification. PCA used in this study was successfully differentiate and classify

scholarly journals Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

2015 ◽  
Vol 5 (1) ◽  
pp. 92 ◽  
Author(s):  
A. B. M. Aowlad Hossain ◽  
Md. Wasiur Rahman ◽  
Manjurul Ahsan Riheen
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Hand Movements ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Beta Band ◽  
Right Hand ◽  
Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

Novel Rehabilitation Devices for Hand Movement Disorders

2011 ◽  
pp. 312-318
Author(s):  
Akira Gyoten ◽  
Jinglong Wu ◽  
Satoshi Takahashi
Keyword(s):  
Movement Disorders ◽  
Hand Movement ◽  
Surface Emg ◽  
Portable Device ◽  
Recovery Effect ◽  
Stroke Patients ◽  
Hand Motion ◽  
Rehabilitation Devices ◽  
At Home

Numerous therapeutic rehabilitation devices have been studied. This chapter describes novel rehabilitation devices designed to treat hand movement disorders. Recently, robot-aided rehabilitation using instruments, such as a hand motion robots and a robotic glove, have attracted interest because they help recover motor function in stroke patients. The lack of proper care for at-home patients is a major problem. The authors of this chapter developed a novel portable device, consisting of two grips, that allows the patient to perform exercises at home. While a patient grasps both grips with one hand, the driving grip reciprocates at several speed adjustments. The relative distance between the movable and fixed grip enables the hand to open. In addition, a master-slave system that measures the surface EMG on the healthy arm is proposed for self-controlled rehabilitation therapy. This portable device is not complex and can be used without assistance. Future development will improve the quality of the system, and the recovery effect will be evaluated in clinical trials.

A Stereometric System for Measuring Human Motion

1970 ◽  
Vol 12 (6) ◽  
pp. 523-535 ◽  
Author(s):  
M. A. Ayoub ◽  
M. M. Ayoub ◽  
J. D. Ramsey
Keyword(s):  
Performance Studies ◽  
Human Performance ◽  
Low Cost ◽  
Human Motion ◽  
Human Hand ◽  
Hand Motion ◽  
Cost System ◽  
Laboratory Investigations ◽  
Texas Tech University ◽  
Design Construction

Although several photogrammetric systems are commercially available, their cost limits the application of photogrammetric human factors studies. This paper describes a relatively low cost system developed at Texas Tech University to be used in connection with biomechanics and human performance studies. A detailed description of the basic theoretical and laboratory investigations of the various parameters which influence the design, construction, and use of the system is presented. The adequacy and accuracy of the system were measured by conducting two verification tests under static and dynamic orientations. Typical acceleration and velocity curves for human hand motion, obtained by the system, are presented.

scholarly journals Design of Hand Motion Assist Robot for Rehabilitation Physiotherapy

2015 ◽  
Vol 4 (04) ◽  
pp. 07-11
Author(s):  
Mr. Kunal Verma, Mr. Dharmesh Dhabliya
Keyword(s):  
Angular Distance ◽  
Human Hand ◽  
Stator Winding ◽  
Body Parts ◽  
Servo Motor ◽  
Hand Motion ◽  
Main Drawback ◽  
Mobility Function ◽  
Left And Right ◽  
Humanoid Arm

This paper deals about developing a microcontroller based two-axis robot for human hand physiotherapy for the treatment of paralyzed patients. The interactive two-axis motion robot is designed to fit around patient’s arm and work with the patient to reestablish movements of hand by gently moving it in desired direction. The robot moves the patients hand up and down, left and right using servo motor interfaced with it. The motor is controlled by switching ON/OFF the stator winding. The microcontroller generates the switching pulses of the motor; the angular distance and movements are programmable through keys. The robot actively moves the non-responsive body parts allowing it to be a useful tool in all steps of rehabilitation. Note to Practitioners-Compared with cable-driven humanoid arm, a cable less robot is more accurate because a cable driven robot has some drawbacks due to the mismatch of connections. If the connections are mishandled, there is a chance to occur any severe damages. The main drawback is more expensive, very difficult to maintain and clean. This drawback can be rectified by the proposed method. The main feature of this robot is its mobility function.

scholarly journals Evaluation of Optical and Radar Based Motion Capturing Technologies for Characterizing Hand Movement in Rheumatoid Arthritis—A Pilot Study

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1208
Author(s):  
Uday Phutane ◽  
Anna-Maria Liphardt ◽  
Johanna Bräunig ◽  
Johann Penner ◽  
Michael Klebl ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Continuous Wave ◽  
Hand Function ◽  
Objective Assessment ◽  
Hand Movement ◽  
Human Motion ◽  
Limiting Factors ◽  
Human Hand ◽  
Wave Radar ◽  
Patient Reported

In light of the state-of-the-art treatment options for patients with rheumatoid arthritis (RA), a detailed and early quantification and detection of impaired hand function is desirable to allow personalized treatment regiments and amend currently used subjective patient reported outcome measures. This is the motivation to apply and adapt modern measurement technologies to quantify, assess and analyze human hand movement using a marker-based optoelectronic measurement system (OMS), which has been widely used to measure human motion. We complement these recordings with data from markerless (Doppler radar) sensors and data from both sensor technologies are integrated with clinical outcomes of hand function. The technologies are leveraged to identify hand movement characteristics in RA affected patients in comparison to healthy control subjects, while performing functional tests, such as the Moberg-Picking-Up Test. The results presented discuss the experimental framework and present the limiting factors imposed by the use of marker-based measurements on hand function. The comparison of simple finger motion data, collected by the OMS, to data recorded by a simple continuous wave radar suggests that radar is a promising option for the objective assessment of hand function. Overall, the broad scope of integrating two measurement technologies with traditional clinical tests shows promising potential for developing new pathways in understanding of the role of functional outcomes for the RA pathology.

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