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Tactile display on the remaining hand for unilateral hand amputees

  • Tao Li EMAIL logo , Huaiqi Huang , Christian Antfolk , Jörn Justiz and Volker M. Koch
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Human rely profoundly on tactile feedback from fingertips to interact with the environment, whereas most hand prostheses used in clinics provide no tactile feedback. In this study we demonstrate the feasibility to use a tactile display glove that can be worn by a unilateral hand amputee on the remaining healthy hand to display tactile feedback from a hand prosthesis. The main benefit is that users could easily distinguish the feedback for each finger, even without training. The claimed advantage is supported by preliminary tests with healthy subjects. This approach may lead to the development of effective and affordable tactile display devices that provide tactile feedback for individual fingertip of hand prostheses.

Keywords: hand prosthesis; tactile display; unilateral amputation

1 Introduction

With versatile sensory and motor capacities, hands are the most dexterous body parts to interact with the environment. Hand amputation, mostly caused by trauma events or diseases, causes lots of inconvenience into everyday life. Commercial hand prostheses partially restore the hand motor functions, but in most cases not the sensory abilities [1]. Academic research on tactile feedback for hand prostheses has provided various solutions. With the recent breakthroughs, invasive tactile feedback approaches to provide tactile feedback [2], [3] would provide the best results in the long run. With less risks, noninvasive approaches [4] are expected to benefit hand prostheses users in the near future.

Previous research has shown that noninvasively displaying tactile information generated at the five fingertips of a hand prosthesis by a miniature tactile display device is not trivial to achieve. Except on hands and faces, the two point discrimination distance on human body surface is mostly beyond 3 cm [5]. Therefore, small areas such as the upper arm is in principle not sufficient to noninvasively display the information of five distinct locations. Another challenge is loss of reference: subjects have frequently reported to lose tracking of tactile feedback locations [6]. Applying tactile feedback to a larger body area, such as the back or around the waist, shows good results of perception, but the devices are bulky and awkward to use.

We propose in this study to use the remaining healthy hand as the location to apply tactile feedback for prostheses used by unilateral hand amputees. Our hypotheses about this tactile display approach are: distinguishing five tactile feedback locations would be easy to achieve with little or even no training; confusion among fingers could initially be a challenge, but will be overcome during the device usage. Although there have been tactile feedback gloves available on the market, these gloves mostly provide feedback on fingertips and/or the palm. Tactile gloves designed with this approach would interfere with the normal sensory and motor functions of the healthy hand when used as a tactile feedback device for amputees. In this study, we designed a tactile feedback glove which provides feedback on the back of fingers to reduce influences.

In what follows, our proposed tactile feedback concept and the designed glove is presented first. Then experiments designed to evaluate the effectiveness of the tactile feedback device are described. Finally, we analyze the results and discuss the implications.

2 Tactile feedback device

Although the dorsal sides of fingers are not as sensitive as the ventral, localizing on which finger a physical stimulation is applied is still quite intuitive. To deliver the tactile information of a hand prosthesis for unilateral hand amputees, a tactile feedback glove is fabricated and to be worn on the remaining healthy hand. The glove delivers tactile information generated on fingertips of a hand prosthesis to the back of corresponding healthy fingers of the remaining healthy hand. The main design objectives are: intuitive tactile information display, a compact size, light weight, and easy to wear. The tactile feedback design concept and designed glove are shown in Figure 1.

Figure 1 Tactile feedback concept. A tactile display glove is worn on the remaining right healthy hand by a unilateral hand amputee to display tactile events generated on the fingertips of the hand prosthesis (left). The tactile display actuators (pancake vibrators, indicated by white dash circles) are fixed on the glove and placed at the back of each finger.
Figure 1

Tactile feedback concept. A tactile display glove is worn on the remaining right healthy hand by a unilateral hand amputee to display tactile events generated on the fingertips of the hand prosthesis (left). The tactile display actuators (pancake vibrators, indicated by white dash circles) are fixed on the glove and placed at the back of each finger.

The tactile feedback glove (shown in Figure 1) mainly consists of a sport glove, five eccentric rotating mass (ERM) vibrators (iNeed Inc., HK), and control electronics. A sport glove fits tightly to the fingers, which is essential to reliably deliver vibrotactile information to the back of fingers. A sport glove is also easy to take on and off, which is an important consideration to facilitate the independent usage by hand amputees. The ERM vibrators have the advantage of low power consumption, compact sizes (diameter 10 mm, thickness 3.4 mm), low costs, and an easy control interface. These vibrators provide tactile feedback at the back of each fingers to minimize the influence to the motor and sensory functions of the healthy hand.

3 Material and methods

To preliminarily evaluate the effectiveness of the tactile feedback concept described in the previous section, we designed experiments to test the tactile feedback glove with healthy subjects. These experiments have been approved by the institutional ethics committee of the Lund University in Sweden.

3.1 Test subjects

Five healthy subjects participated in the experiments. Prior to experiments, they were informed about the procedure and possible risks of the study verbally by an investigator and by experiment information sheets. All of them have signed the participant consent forms. The subjects were 26 ± 8 years old (one female and four males). All of them are right-handed and have declared to have normal perception abilities.

3.2 Experiment setting

In preparing an experiment, a test subject sits on a chair and rests her/his right arm on a table in front of her/him. The tactile feedback glove is worn on the subject’s right hand and adjusted to fit the hand well. During an experiment, a test subject is asked to move his/her right hand around while perceiving tactile information applied on the back of fingers to mimic our intended application scenarios. Hand movements increase the difficulty to identify the tactile feedback applied by the glove. An investigator issues stimulation signals by a piece of control software running on a laptop. The software interface is shown in Figure 2. The subject reports verbally his/her judgments about the tactile feedback to the investigator. The investigator records the answers on data record forms.

Figure 2 The software interface used to issue stimulations during experiments. An investigator gives tactile feedback signals by clicking on one of the three buttons on each finger. The three buttons correspond to three feedback levels: strong (S), medium (M), and weak (W). The activated feedback is indicated both by a blue color on the button and the status panel.
Figure 2

The software interface used to issue stimulations during experiments. An investigator gives tactile feedback signals by clicking on one of the three buttons on each finger. The three buttons correspond to three feedback levels: strong (S), medium (M), and weak (W). The activated feedback is indicated both by a blue color on the button and the status panel.

3.3 Experiment procedure

The experiment consists of two sessions: the first session tests the capacity of the tactile feedback glove to display tactile information of five individual fingers; the second evaluates the device’s potential to transfer contact force strength information. We expect that the tactile feedback glove could display tactile information intuitively, therefore no training session is included. No feedback about correct answers is given to subjects during the tests.

During the first experiment session, each subject is given six groups of vibrotactile stimulations of different durations with the order of: (1) 250 ms, (2) 100 ms, (3) 80 ms, (4) 1000 ms, (5) 50 ms, and (6) 500 ms. Each group of stimulations contains 25 vibratactile feedbacks of the same strength (medium level) from the glove. Each finger is given an equal number of five tactile stimulation signals. The 25 stimulations are given in a random order. The subject should answer the perceived stimulation location as fast as possible and the total testing time of each stimulation group is recorded.

During the second session, each subject is given 15 vibrotactile stimulation of three strength levels (strong, medium, or weak) on the same finger (index). The stimulation duration is fixed to 100 ms. Each of the three stimulation levels are repeated for five times in a random order. The subject should answer the perceived stimulation level as quick as possible. The total time to finish the session is recorded.

4 Results

Experiment results are presented in this section as a preliminary evaluation of the tactile feedback concept proposed in this study.

4.1 Finger detection

The error rate and adjusted response time (excluding stimulation duration) of distinguishing tactile feedback stimulations applied on the back of five fingers without training is shown in Figure 3. One can observe that in general both the error rate and response time decrease as the stimulation duration increases. With a stimulation duration of 100 ms, subjects have managed to distinguish the majority of displayed finger localization information correctly.

Figure 3 The error rate and response time (solid error bar) to localize tactile display on the back of fingers. The error rate is high when applying a short stimulation duration of 50 ms and the error rate reduces when the stimulation duration is larger than 100 ms. The response time tends to decrease as the stimulation duration increases.
Figure 3

The error rate and response time (solid error bar) to localize tactile display on the back of fingers. The error rate is high when applying a short stimulation duration of 50 ms and the error rate reduces when the stimulation duration is larger than 100 ms. The response time tends to decrease as the stimulation duration increases.

4.2 Force level detection

The experiment results of test subjects using the tactile feedback glove to perceive different levels of stimulation is shown in Figure 4. Because the subjects were tested without a training session in our study, they needed to learn the relative stimulation strength during experiments. It was observed that the medium feedback level was most frequently misjudged.

Figure 4 The error rate (A) and confusion matrix (B) of distinguishing three stimulation levels displayed on the back of the index finger. The medium stimulation level is the most difficult to recognize. It tends to be mistakenly recognized as the strong feedback. Note that the confusion matrix includes data from all five subjects, so there are 25 tests for each stimulation level.
Figure 4

The error rate (A) and confusion matrix (B) of distinguishing three stimulation levels displayed on the back of the index finger. The medium stimulation level is the most difficult to recognize. It tends to be mistakenly recognized as the strong feedback. Note that the confusion matrix includes data from all five subjects, so there are 25 tests for each stimulation level.

4.3 Learning effect

Although displaying tactile information on the back of fingers does not feel as natural as on fingertips, response time shows subjects gradually get used to this tactile information display approach. As shown in Figure 5, subjects’ response times tend to decrease as the experiments proceed. We also observed during the second experiment session that mistakes for force level detection happened mostly at the beginning. Moreover, test subjects also reported that they could perceive the information transferred by stimulations easier over time. However, the result tends to be biased by the stimulation duration: durations of 500 ms and 1000 ms also have the shortest response time.

Figure 5 Learning effect observed during the six test groups of experiment session one. Test subjects’ response time generally decreases during the session as the experiment proceeds. The stimulation durations of test groups #1 to #6 are: 250 ms, 100 ms, 80 ms, 1000 ms, 50 ms, and 500 ms, respectively. The increased response time of tests #5 and #6 can be explained by the difficulty to detect the ERM stimulation duration of 50 ms.
Figure 5

Learning effect observed during the six test groups of experiment session one. Test subjects’ response time generally decreases during the session as the experiment proceeds. The stimulation durations of test groups #1 to #6 are: 250 ms, 100 ms, 80 ms, 1000 ms, 50 ms, and 500 ms, respectively. The increased response time of tests #5 and #6 can be explained by the difficulty to detect the ERM stimulation duration of 50 ms.

5 Discussion and conclusion

Our preliminary experiments on five healthy subjects show that displaying tactile information on the back of fingers is feasible and intuitive. The displayed information (finger localization and contact force level) can be readily recognized even without training.

Even though it seems to be disturbing to display tactile events occurred at fingertips of the left hand at the back of the right hand, our study shows that subjects learn the tactile display quickly. The adaptability of human body surface to new tactile information has also been observed in previous studies [7].

The tactile feedback concept proposed in this study is to be evaluated with amputee subjects. We expect that this tactile display method will work well with unilateral hand amputees. When a hand amputee subject manipulates objects with a hand prosthesis, the tactile events are generated actively. As a result, the amputee has a prediction of the tactile feedback before it happens. Therefore, an actively generated tactile event is probably even easier to recognize than a passive one.

Acknowledgement

The authors would like to thank Mr. Adrian Stirnimann for fabricating the tactile feedback glove prototype.

Author’s Statement

Research funding: This study was Funded by Nano-Tera.ch, financed by the Swiss Confederation, and scientifically evaluated by the SNSF under the RTD WiseSkin Project (Grant No. 20NA21_143070). Conflict of interest: Authors state no conflict of interest. Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Tao Li et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0089
Keywords for this article
hand prosthesis; tactile display; unilateral amputation
Creative Commons
BY-NC-ND 4.0

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  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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