DocumentCode :
6527
Title :
Extraction of Time and Frequency Features From Grip Force Rates During Dexterous Manipulation
Author :
Mojtahedi, Keivan ; Qiushi Fu ; Santello, Marco
Author_Institution :
Neural Control of Movement Lab., Arizona State Univ., Tempe, AZ, USA
Volume :
62
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
1363
Lastpage :
1375
Abstract :
The time course of grip force from object contact to onset of manipulation has been extensively studied to gain insight into the underlying control mechanisms. Of particular interest to the motor neuroscience and clinical communities is the phenomenon of bell-shaped grip force rate (GFR) that has been interpreted as indicative of feedforward force control. However, this feature has not been assessed quantitatively. Furthermore, the time course of grip force may contain additional features that could provide insight into sensorimotor control processes. In this study, we addressed these questions by validating and applying two computational approaches to extract features from GFR in humans: 1) fitting a Gaussian function to GFR and quantifying the goodness of the fit [root-mean-square error, (RMSE)]; and 2) continuous wavelet transform (CWT), where we assessed the correlation of the GFR signal with a Mexican Hat function. Experiment 1 consisted of a classic pseudorandomized presentation of object mass (light or heavy), where grip forces developed to lift a mass heavier than expected are known to exhibit corrective responses. For Experiment 2, we applied our two techniques to analyze grip force exerted for manipulating an inverted T-shaped object whose center of mass was changed across blocks of consecutive trials. For both experiments, subjects were asked to grasp the object at either predetermined or self-selected grasp locations (“constrained” and “unconstrained” task, respectively). Experiment 1 successfully validated the use of RMSE and CWT as they correctly distinguished trials with versus without force corrective responses. RMSE and CWT also revealed that grip force is characterized by more feedback-driven corrections when grasping at self-selected contact points. Future work will examine the application of our analytical approaches to a broader range of tasks, e.g., assessment of recovery of sensorimotor function following clini- al intervention, interlimb differences in force control, and force coordination in human-machine interactions.
Keywords :
Gaussian processes; biomechanics; feature extraction; feedforward; force measurement; medical signal processing; wavelet transforms; Gaussian function; Mexican Hat function; bell-shaped grip force rate; continuous wavelet transform; feedback-driven corrections; feedforward force control; force control; force coordination; frequency feature extraction; human-machine interactions; root-mean-square error; sensorimotor control; time feature extraction; Analysis of variance; Feature extraction; Feedforward neural networks; Force; Force control; Loading; Continuous wavelet transform (CWT); feedback; feedforward; hand; sensorimotor memory;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2015.2388592
Filename :
7004021
Link To Document :
بازگشت