Evaluation of Pressure Capacitive Sensors for Application in Grasping and Manipulation Analysis

The analysis of the human grasping and manipulation capabilities is paramount for investigating human sensory-motor control and developing prosthetic and robotic hands resembling the human ones. A viable solution to perform this analysis is to develop instrumented objects measuring the interaction forces with the hand. In this context, the performance of the sensors embedded in the objects is crucial. This paper focuses on the experimental characterization of a class of capacitive pressure sensors suitable for biomechanical analysis. The analysis was performed in three loading conditions (Distributed load, 9 Tips load, and Wave-shaped load, thanks to three different inter-elements) via a traction/compression testing machine. Sensor assessment was also carried out under human- like grasping condition by placing a silicon material with the same properties of prosthetic cosmetic gloves in between the sensor and the inter-element in order to simulate the human skin. Data show that the input–output relationship of the analyzed, sensor is strongly influenced by both the loading condition (i.e., type of inter-element) and the grasping condition (with or without the silicon material). This needs to be taken into account to avoid significant measurement error. To go over this hurdle, the sensors have to be calibrated under each specific condition in order to apply suitable corrections to the sensor output and significantly improve the measurement accuracy.

[1]  E. Guglielmelli,et al.  Torque-dependent compliance control in the joint space of an operational robotic machine for motor therapy , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[2]  W D Memberg,et al.  Instrumented objects for quantitative evaluation of hand grasp. , 1997, Journal of rehabilitation research and development.

[3]  Jianwei Zhang,et al.  Multi-sensor based segmentation of human manipulation tasks , 2010, 2010 IEEE Conference on Multisensor Fusion and Integration.

[4]  Loredana Zollo,et al.  Slippage Detection with Piezoresistive Tactile Sensors , 2017, Sensors.

[5]  T. Hasegawa,et al.  A decision method for the placement of mechanical tactile elements for grasp type recognition , 2008, 2008 IEEE Sensors.

[6]  Johan L van Leeuwen,et al.  Usability of normal force distribution measurements to evaluate asymmetrical loading of the back of the horse and different rider positions on a standing horse. , 2009, Veterinary journal.

[7]  Horst Peter Wölfel,et al.  Apparent mass of seated man—First determination with a soft seat and dynamic seat pressure distributions , 2006 .

[8]  Philip J.W. Hands,et al.  Design, Manufacture and Testing of Capacitive Pressure Sensors for Low-Pressure Measurement Ranges , 2017, Micromachines.

[9]  Loredana Zollo,et al.  Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention , 2016 .

[10]  L. Baur,et al.  What are the effects of obesity in children on plantar pressure distributions? , 2004, International Journal of Obesity.

[11]  M. A. Roa,et al.  Experimental evaluation of human grasps using a sensorized object , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[12]  A. Kargov,et al.  A comparison of the grip force distribution in natural hands and in prosthetic hands , 2004, Disability and rehabilitation.

[13]  Francesca Cordella,et al.  Design and development of a sensorized cylindrical object for grasping assessment , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Bruce D Beynnon,et al.  Efficacy of Plantar Loading Parameters During Gait in Terms of Reliability, Variability, Effect of Gender and Relationship Between Contact Area and Plantar Pressure , 2005, Foot & ankle international.

[15]  Máximo A. Roa,et al.  Grasp quality measures: review and performance , 2014, Autonomous Robots.

[16]  P Berlit,et al.  Dynamic plantar pressure distribution measurements in hemiparetic patients. , 1997, Clinical biomechanics.

[17]  Sarah A Curran,et al.  Research and development at novel GmbH, Germany for prosthetics and paraplegics , 2012, Prosthetics and orthotics international.

[18]  Olga Troynikov,et al.  Evaluation of Flexible Force Sensors for Pressure Monitoring in Treatment of Chronic Venous Disorders , 2017, Sensors.

[19]  Tao Chen,et al.  The Design and Characterization of a Flexible Tactile Sensing Array for Robot Skin , 2016, Sensors.

[20]  E. Iso,et al.  Measurement Uncertainty and Probability: Guide to the Expression of Uncertainty in Measurement , 1995 .

[21]  Sanford G. Meek,et al.  Improved Grasp Force Sensitivity for Prosthetic Hands Through Force-Derivative Feedback , 2008, IEEE Transactions on Biomedical Engineering.

[22]  Nikolaos G. Tsagarakis,et al.  The patched intrinsic tactile object: A tool to investigate human grasps , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Miltiadis Boboulos Automation and Robotics , 2010 .

[24]  J L Cunningham,et al.  A comparison of vertical force and temporal parameters produced by an in-shoe pressure measuring system and a force platform. , 2000, Clinical biomechanics.

[25]  Bruno Siciliano,et al.  A force-and-slippage control strategy for a poliarticulated prosthetic hand , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[26]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[27]  Francesca Cordella,et al.  Development and preliminary testing of an instrumented object for force analysis during grasping , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[28]  Helge J. Ritter,et al.  Analysis of human grasping under task anticipation using a tactile book , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[29]  Marco Santello,et al.  Multidigit force control during unconstrained grasping in response to object perturbations. , 2017, Journal of neurophysiology.

[30]  Bruno Siciliano,et al.  Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand , 2014 .

[31]  Patrick McLaughlin,et al.  Total contact cast wall load in patients with a plantar forefoot ulcer and diabetes , 2016, Journal of Foot and Ankle Research.