Human Hand Motion Analysis during Different Eating Activities

Table 1

Highlights of the previous contributions to human motion analysis.

Number	Authors	Objective	Focus of study	Data acquisition method	Results/findings	Activity
1	Ju and Liu [11]	To correlate the muscle signals with contact forces and finger trajectories & motion recognition using muscle signals	Human hand motion analysis with multisensory information	EMG sensor, force sensor & DataGlove	Strong 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
2	Gopura et al. [12]	To analyse upper-limb muscle activities during basic upper-limb motion, to design power-assist robotic exoskeleton systems	Human upper-limb muscle activities during daily upper-limb motions	EMG electrodes, VICON motion capture system	Relationships between the upper limb motions & activity levels of main muscles have been established	Basic motions and the selected daily activities of upper-limb
3	Tang et al. [13]	To classify multiple hand gestures using three different methods	Hand motion classification using a multichannel surface sEMG sensor	sEMG sensors	Experimental results showed that the success rate for the identification of all the 11 gestures is significantly high	11 hand gestures
4	Cabibihan et al. [14]	To analyse the gesture, the amount of force applied on regions of the hand, and the angular motion of finger joints	Human patting gesture analysis for robotic social touching	CyberGlove II; FingerTPS sensors	The sensitive regions on the hand while performing pat have been identified	Human patting gesture
5	Rosen et al. [15]	To study the kinematics and the dynamics of the human arm during daily activities	The human arm kinematics and dynamics during daily activities	VICON motion capture system & reflective markers	The results indicated that the various joints’ kinematics and dynamics change significantly based on the nature of the task	24 ADL
6	Ah et al. [16]	To evaluate motor control abilities between the groups of people with mild and moderate arm impairments	3D kinematic motion analysis of door handling task in people with mild and moderate stroke	VICON motion capture system & reflective markers	Comparisons have been drawn between healthy, mild & moderate stroke patients	Door handling task
7	Aprile et al. [17]	To analyse, using motion analysis, the qualitative and quantitative upper limb motor strategies in stroke patients	Kinematic analysis of the upper limb motor strategies in stroke	Smart motion capture optoelectronic system	Comparisons have been drawn between stroke & healthy control group while reaching out for the glass to drink	Drinking task
8	Adnan et al. [18]	To develop a low-cost DataGlove, able to recognize the different finger activities	Measurement of the flexible bending force of the index and middle fingers for virtual interaction	Low-cost DataGlove by using the flexible bending sensor	The DataGlove developed can measure several human degrees of freedom (DoFs)	Sign language translation (letters A, B, C, D, F & K and number 8)
9	Adnan et al. [19]	To find the correlations between the forces of finger phalanges	Accurate measurement of force by the force sensor for intermediate and proximal phalanges of index finger	Flexiforce pressure sensors	An 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 DataGlove	Any finger gripping activity