Spatio-Temporal Human Grip Force Analysis via Sensor Arrays (original) (raw)
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Detection of changes in grip forces on a sliding object
Journal of Neuroscience Methods, 2007
Holding a slipping object in hand requires adjustment of grip forces. The aim of the study was to develop a method for measuring the temporal and spatial distribution of grip forces during the holding of a slipping object in the hand. A special grip rod with a measuring film containing 200 resistor-based pressure sensors equally distributed over 50 cm 2 was developed, providing a system that has a spatial resolution of 5 mm, a temporal resolution of 1/150 Hz and a force resolution 0.05 N. A force-change-detection algorithm was constructed to detect and separate pressure and position of individual fingers. The algorithm is a modification of a classical Gaussian random field theory algorithm for detecting significant data [Rogerson PA. Change detection thresholds for remotely sensed images. J Geog Syst 2002;4:85-97]. The modification takes the signal strength into account to reduce false positive detection in low grip force situations. The grip force measuring system and the force-change-detection algorithm allow measurement of the forces exerted by any number of fingers simultaneously without any constraints on finger position and are suitable for basic and clinical research in human and animal physiology as well as for psychophysics studies.
Sensor evaluation for hand grip strength
International Journal of Electrical and Computer Engineering (IJECE), 2022
This paper discusses the evaluation of the sensors used in the hand grip strength glove. The glove comprises of flex and force resisting sensors. Force resisting sensor determines the force applied by various parts of the palm, while the flex sensor determines the flexion of the fingers. These sensors are placed in a specific position on the glove to obtain correct data when the glove is used. The glove has two modes, which are pencil grip mode and object grip mode. The sensors determine which mode the glove is in depending on the gesture made. The glove is examined using a pencil and a cylindrical object to evaluate the strength of the grip. After gripping the object or pencil, the system evaluates the force applied using the sensors. This data is transferred to a computer for further analysis using a trained model. The model was able to achieve an accuracy of 90.8%.
Exploring the Human Grip Force System: A Preliminary Study
² It has been postulated that cutaneo-receptors from the digits contribute to the control of manipulatory functions of the hand. However, few studies have examined the dynamics of the human grip force system (HGFS) in terms of the dynamic relationship between the vertical loads, taken as an input and the resulting grip force, taken as the output. This paper describes the experimental procedures we have developed to explore the HGFS and presents some initial experimental results. These demonstrate that the step response of the HGFS is biphasic. The first, short latency phase, likely involves only passive and intrinsic components. The second, longer latency phase is likely related to reflex mechanisms since it is preceded by a strong burst of EMG. Moreover, the HGFS response depended nonlinearly on the amplitude and direction of the load applied. These results indicate that further investigation of HGFS dynamics will require the use of nonlinear identification methods.
A system for measuring finger forces during grasping
2000
A device that provides quantitative assessment of the grasping function and allows the grasping function improvements to be monitored over time can potentially be very useful for hand surgeons, physiotherapists and occupational therapists. A Dynamic Grasping Assessment System (DGAS) that is capable of measuring finger forces during wrist extension, flexion, adduction and abduction was developed. The DGAS can measure forces for each individual finger in the range from 0 to 125 N with accuracy of ± 0.5 N, and can measure the wrist extension/flexion angle and the wrist ulnar/radial abduction angle with an accuracy of ± 0.25 deg. Furthermore the DGAS is capable of generating resistive torque during the wrist motion and allows to assess finger forces during wrist motion against resistive load. The DGAS can provide the following data: (1) finger forces as a function of time, wrist angle, wrist angular velocity and resistive load;
Quantification of hand grasp force using a pressure mapping system
The goal of this study was to use a pressure sensor to measure the force distribution and contact area of the hand when gripping, pushing, and pulling a cylinder. Data was collected from 10 subjects with no hand impairments and from 1 subject with rheumatoid arthritis (RA). Subjects grasped an aluminum cylinder wrapped with a Tekscan pressure sensor and performed each trial at 25%, 50%, and 100% maximum voluntary exertion. A relationship was found between increasing exertion and increasing hand area with increasing hand contact area. The force distribution maps showed the thenar region of the hand exerts the most force during pushing while the metacarpal joint line exerts the highest force during pulling. The third and fourth phalange were found to exert the highest phalange force during gripping. The force distribution maps from the RA subject showed higher thumb forces and distal phalange forces, relative to the entire phalange, compared to the non-impaired subjects. This suggests that the RA subject compensates for the lack of phalange function with the regions of the hand that still function. Future studies should sample individuals with a larger hand area range and sample more individuals with RA.
A conductive polymer sensor for measuring external finger forces
Journal of Biomechanics, 1991
This paper describes the construction and use of a durable and thin force sensor that can be attached to the palmar surface of the fingers and hands for studying the biomechanics of grasp and for use in hand injury rehabilitation. These force sensors were constructed using a mod&d commercially available conductive polymer pressure sensing element and installing an epoxy dome for directing applied forces through a 12 mm diameter active sensing area. The installation of an epoxy dome was effective for making the sensors insensitive to contact surfaces varying from 25 to 1100 mm* and a 16 mm radius surface curved convex towards the finger. The completed sensors were only 1.8 mm thick and capable of being taped to the distal phalangeal finger pads. They were calibrated on the hand by pinching a strain gage dynamometer. The useful range was between 0 and 30 N with an accuracy of 1 N for both static loading and normal dynamic grasp activities. The sensor time constant was 0.54 ms for a step force input. Because of varying offset voltages every time the sensors were attached, these sensors should be calibrated on the hand before each use. The sensors were used for measuring finger forces during controlled pinching and lifting tasks, and during ordinary grasping activities, such as picking up a book or a box, where the useful force range and response for these sensors were adequate.
The objective of this work was the comparative analysis of two commercial force sensors, the FSR sensors (Interlink Electronics, Camarillo, CA, US) and the Flexiforce (Tekscan Inc., Boston, MA, US), that can be attached to the palmar surface of the fingers to study the biomechanics of the hand during grasp and to develop rehabilitative devices such as closed-loop controlled hand neuroprostheses. The performance tests were performed with the sensors mounted on the thumb. The Flexiforce sensors showed better performance in terms of repeatability, linearity and time drift when mounted on a rigid substrate, and in terms of dynamic accuracy when mounted on the thumb. The FSR sensors showed better performance in terms of robustness.
Bioengineering, 2020
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. T...
Sensors (Basel, Switzerland), 2021
Successful grasping with multi-fingered prosthetic or robotic hands remains a challenge to be solved for the effective use of these hands in unstructured environments. To this end, currently available tactile sensors need to improve their sensitivity, robustness, and spatial resolution, but a better knowledge of the distribution of contact forces in the human hand in grasping tasks is also necessary. The human tactile signatures can inform models for an efficient control of the artificial hands. In this study we present and analyze a dataset of tactile signatures of the human hand in twenty-one representative activities of daily living, obtained using a commercial high spatial resolution pressure sensor. The experiments were repeated for twenty-two subjects. The whole dataset includes more than one hundred million pressure data. The effect of the task and the subject on the grip force and the contribution to this grip force made by the different hand regions were analyzed. We also p...
Grip Pressure and Wrist Joint Angle Measurement during Activities of Daily Life
Procedia Manufacturing, 2015
The goal of this study is to improve future physical human-robot interaction and rehabilitation systems. Experiments were conducted to collect dominant hand grip pressure and joint-angle data during activities of daily life. Representative actions chosen as part of this study were: pushing a weighted cylinder along a flat surface, pulling a weighted cylinder across a flat surface, and lifting a weighted cylinder from a flat surface to shoulder height. Three separate weighted cylinders were used, 3lbs., 5lbs., and 10lbs., and the representative motions were repeated five times for each cylinder. A Tekscan Grip VersaTek Pressure Measurement System and Motion Analysis Cortex System were utilized to collect data. Each subject was outfitted with 18 separate sensorized piezo-resistive tiles placed on their dominant hand and 33 reflective markers at representative locations on their body. The motion of each cylinder was tracked via the placement of seven retro-reflective markers on the cylinder's surface. Analysis of cohort data from five male and five female volunteers, aged between 23 and 51 years, is presented. A Moving Average Filter was implemented to automatically determine contact between the subject's hand and the weighted cylinder. Once contact was determined during an action cycle, maximum detected pressure from each of 18 sensing areas was found. Results report wrist angle during the action-cycle as well as maximum applied pressure during each action across the cohort. Average wrist angle per action-cycle, by action, is also reported for the cohort. These data, along with results from a previous study, will be used improve and verify human intent models for use in future pHRI and rehabilitation systems.