Applied Bionics and Biomechanics / 2018 / Article / Tab 1

Research Article

Human Hand Motion Analysis during Different Eating Activities

Table 1

Highlights of the previous contributions to human motion analysis.

NumberAuthorsObjectiveFocus of studyData acquisition methodResults/findingsActivity

1Ju and Liu [11]To correlate the muscle signals with contact forces and finger trajectories & motion recognition using muscle signalsHuman hand motion analysis with multisensory informationEMG sensor, force sensor & DataGloveStrong correlations between muscle signals, contact forces, and finger trajectories.
Fuzzy Gaussian mixture models (FGMMs) used for motion recognition
Ten in-hand manipulations like holding & lifting a dumbbell

2Gopura et al. [12]To analyse upper-limb muscle activities during basic upper-limb motion, to design power-assist robotic exoskeleton systemsHuman upper-limb muscle activities during daily upper-limb motionsEMG electrodes, VICON motion capture systemRelationships between the upper limb motions & activity levels of main muscles have been establishedBasic motions and the selected daily activities of upper-limb

3Tang et al. [13]To classify multiple hand gestures using three different methodsHand motion classification using a multichannel surface sEMG sensorsEMG sensorsExperimental results showed that the success rate for the identification of all the 11 gestures is significantly high11 hand gestures

4Cabibihan et al. [14]To analyse the gesture, the amount of force applied on regions of the hand, and the angular motion of finger jointsHuman patting gesture analysis for robotic social touchingCyberGlove II
FingerTPS sensors
The sensitive regions on the hand while performing pat have been identifiedHuman patting gesture

5Rosen et al. [15]To study the kinematics and the dynamics of the human arm during daily activitiesThe human arm kinematics and dynamics during daily activitiesVICON motion capture system & reflective markersThe results indicated that the various joints’ kinematics and dynamics change significantly based on the nature of the task24 ADL

6Ah et al. [16]To evaluate motor control abilities between the groups of people with mild and moderate arm impairments3D kinematic motion analysis of door handling task in people with mild and moderate strokeVICON motion capture system & reflective markersComparisons have been drawn between healthy, mild & moderate stroke patientsDoor handling task

7Aprile et al. [17]To analyse, using motion analysis, the qualitative and quantitative upper limb motor strategies in stroke patientsKinematic analysis of the upper limb motor strategies in strokeSmart motion capture optoelectronic systemComparisons have been drawn between stroke & healthy control group while reaching out for the glass to drinkDrinking task

8Adnan et al. [18]To develop a low-cost DataGlove, able to recognize the different finger activitiesMeasurement of the flexible bending force of the index and middle fingers for virtual interactionLow-cost DataGlove by using the flexible bending sensorThe DataGlove developed can measure several human degrees of freedom (DoFs)Sign language translation (letters A, B, C, D, F & K and number 8)

9Adnan et al. [19]To find the correlations between the forces of finger phalangesAccurate measurement of force by the force sensor for intermediate and proximal phalanges of index fingerFlexiforce pressure sensorsAn analytical mathematical model and ANOVA has been established to predict the force induced at the flexible force sensor and the human finger of low-cost DataGloveAny finger gripping activity