Mechanical Impedance of the Hand Holding a Spherical Tool at Threshold and Suprathreshold Stimulation Levels

Israr, Ali; Choi, Seungmoon; Tan, Hong Z.

doi:10.1109/whc.2007.81

Cited by 30 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Morioka and Griffin (2005), investigating the effects of contact area and contact location, published vibrotactile detection thresholds using a 3-cm diameter cylindrical handle. Detection thresholds for tools that are common in force-feedback haptic interfaces have also been published, for example, for a stylus (Israr et al, 2006) and a ball (Israr et al, 2007). Compared with these studies, however, a mobile device has a significantly larger contact area, and its vibration stimulates all five fingers and the palm.…”

Section: Human Perception Of Vibrotactile Stimulimentioning

confidence: 99%

“…The vibrations had seven frequencies, 10, 20, 40, 80, 160, 320, and 500 Hz. We selected a frequency set that was identical to the frequencies used in Israr et al (2006Israr et al ( , 2007, to allow direct comparisons of results. These two previous studies reported detection thresholds of vibrations transmitted to the hand through common haptic tools (stylus and sphere, respectively).…”

Section: Stimulimentioning

confidence: 99%

See 1 more Smart Citation

Psychophysical Model for Vibrotactile Rendering in Mobile Devices

Ryu

2010

Presence: Teleoperators and Virtual Environments

View full text Add to dashboard Cite

Vibrotactile rendering is one of the most popular means for improving the user interface of a mobile device, but the availability of related perceptual data that can aid vibrotactile effect design is not currently sufficient. The present paper reports data from a series of psychophysical studies designed to fill this gap. In Experiment I, we measured the absolute detection thresholds of sinusoidal vibrotactile stimuli transmitted to the hand through a mobile phone. Stimuli were generated by a mechanical shaker system that can produce vibrations over a broad frequency and amplitude range. The detection thresholds reported here are a new addition to the literature, and can serve as a baseline for vibrotactile stimulus design. In Experiment II, we estimated the perceived intensities of mobile device vibrations for various frequencies and amplitudes using the same shaker system. We also determined a form of parametric nonlinear function based on Stevens' power law and fit the function to the measured data. This psychophysical magnitude function, which maps vibration frequency and amplitude to a resulting perceived intensity, can be used to predict the perceived intensity of a mobile device vibration from its physical parameter values. In Experiment III, we measured another set of perceived intensities using two commercial miniature vibration actuators (vibration motor and voice-coil actuator) in place of the mechanical shaker. The purpose of this experiment was to evaluate the utility of the psychophysical magnitude function obtained in Experiment II, as vibrotactile stimuli produced by miniature actuators may have different physical characteristics, such as vibration direction and ground condition. Comparison of the results of Experiments II and III confirmed that the psychophysical magnitude function can reliably predict changing trends in the perceived intensity of mobile device vibration. We also discuss further research issues encountered during the investigation. The results presented in this paper may be instrumental in the design of effective vibrotactile actuators and perceptually-salient rendering algorithms for mobile devices.

show abstract

Section: Human Perception Of Vibrotactile Stimulimentioning

confidence: 99%

Section: Stimulimentioning

confidence: 99%

Psychophysical Model for Vibrotactile Rendering in Mobile Devices

Ryu

2010

Presence: Teleoperators and Virtual Environments

View full text Add to dashboard Cite

show abstract

“…McMahan and Kuchenbecker identified a five-parameter MSD model of the hand interfaced with a stylus grip haptic device using a 1-DOF linear actuator custom mounted onto the Phantom's stylus itself (for high-frequency, i.e., 10-200 Hz, vibrotactile feedback applications) [22]. Israr et al have used both stylus-based devices and spherical actuators to shake the hand at 10-500 Hz [23], [24]. Also, Díaz and Gil have investigated the vibration modes from 0.7 to 200 Hz in 1 DOF of the human operator using a racquet grip on the Phantom Premium 1.0 and a custom haptic interface [25].…”

Section: Introductionmentioning

confidence: 99%

Human-Arm-and-Hand-Dynamic Model With Variability Analyses for a Stylus-Based Haptic Interface

Çavuşoğlu

2012

IEEE Trans. Syst., Man, Cybern. B

View full text Add to dashboard Cite

Haptic interface research benefits from accurate human arm models for control and system design. The literature contains many human arm dynamic models but lacks detailed variability analyses. Without accurate measurements, variability is modeled in a very conservative manner, leading to less than optimal controller and system designs. This paper not only presents models for human arm dynamics but also develops inter- and intrasubject variability models for a stylus-based haptic device. Data from 15 human subjects (nine male, six female, ages 20-32) were collected using a Phantom Premium 1.5a haptic device for system identification. In this paper, grip-force-dependent models were identified for 1-3-N grip forces in the three spatial axes. Also, variability due to human subjects and grip-force variation were modeled as both structured and unstructured uncertainties. For both forms of variability, the maximum variation, 95 %, and 67 % confidence interval limits were examined. All models were in the frequency domain with force as input and position as output. The identified models enable precise controllers targeted to a subset of possible human operator dynamics.

show abstract

“…12) and the rotated angle of the pen (φ in Fig. 12) was used as an indicator of solidity [29,30]. The system for simultaneously measuring force and position data is shown in Fig.…”

Section: Solidity Performance Experimental Data Analysis Methodsmentioning

confidence: 99%

Development and assessment of a hand assist device: GRIPIT

Kim

Lee

et al. 2017

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background: Although various hand assist devices have been commercialized for people with paralysis, they are somewhat limited in terms of tool fixation and device attachment method. Hand exoskeleton robots allow users to grasp a wider range of tools but are heavy, complicated, and bulky owing to the presence of numerous actuators and controllers. The GRIPIT hand assist device overcomes the limitations of both conventional devices and exoskeleton robots by providing improved tool fixation and device attachment in a lightweight and compact device. GRIPIT has been designed to assist tripod grasp for people with spinal cord injury because this grasp posture is frequently used in school and offices for such activities as writing and grasping small objects. Methods: The main development objective of GRIPIT is to assist users to grasp tools with their own hand using a lightweight, compact assistive device that is manually operated via a single wire. GRIPIT consists of only a glove, a wire, and a small structure that maintains tendon tension to permit a stable grasp. The tendon routing points are designed to apply force to the thumb, index finger, and middle finger to form a tripod grasp. A tension-maintenance structure sustains the grasp posture with appropriate tension. Following device development, four people with spinal cord injury were recruited to verify the writing performance of GRIPIT compared to the performance of a conventional penholder and handwriting. Writing was chosen as the assessment task because it requires a tripod grasp, which is one of the main performance objectives of GRIPIT. Results: New assessment, which includes six different writing tasks, was devised to measure writing ability from various viewpoints including both qualitative and quantitative methods, while most conventional assessments include only qualitative methods or simple time measuring assessments. Appearance, portability, difficulty of wearing, difficulty of grasping the subject, writing sensation, fatigability, and legibility were measured to assess qualitative performance while writing various words and sentences. Results showed that GRIPIT is relatively complicated to wear and use compared to a conventional assist device but has advantages for writing sensation, fatigability, and legibility because it affords sufficient grasp force during writing. Two quantitative performance factors were assessed, accuracy of writing and solidity of writing. To assess accuracy of writing, we asked subjects to draw various figures under given conditions. To assess solidity of writing, pen tip force and the angle variation of the pen were measured. Quantitative evaluation results showed that GRIPIT helps users to write accurately without pen shakes even high force is applied on the pen.

show abstract

Mechanical Impedance of the Hand Holding a Spherical Tool at Threshold and Suprathreshold Stimulation Levels

Cited by 30 publications

References 17 publications

Psychophysical Model for Vibrotactile Rendering in Mobile Devices

Psychophysical Model for Vibrotactile Rendering in Mobile Devices

Human-Arm-and-Hand-Dynamic Model With Variability Analyses for a Stylus-Based Haptic Interface

Development and assessment of a hand assist device: GRIPIT

Contact Info

Product

Resources

About