| 1. |
- Huang, Huaiqi, et al.
(författare)
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Automatic hand phantom map detection methods
- 2015
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Ingår i: IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. - 9781479972333
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Konferensbidrag (refereegranskat)abstract
- Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.
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| 2. |
- Huang, Huaiqi, et al.
(författare)
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Automatic hand phantom map generation and detection using decomposition support vector machines
- 2018
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Ingår i: BioMedical Engineering Online. - : Springer Science and Business Media LLC. - 1475-925X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is a need for providing sensory feedback for myoelectric prosthesis users. Providing tactile feedback can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person. Methods: In this paper, automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed, complemented by fuzzy logic and active learning strategies. The algorithms and methods are tested on two databases: the first one includes 400 generated phantom maps, whereby the phantom map generation algorithm was based on our observation of the phantom maps to ensure smooth phantom digit edges, variety, and representativeness. The second database includes five reported phantom map images and transformations thereof. The accuracy and training/ classification time of each algorithm using a dense stimulation array (with 100 $$\times $$ × 100 actuators) and two coarse stimulation arrays (with 3 $$\times $$ × 5 and 4 $$\times $$ × 6 actuators) are presented and compared. Results: Both generated and reported phantom map images share the same trends. Majority-pooling sampling effectively increases the training size, albeit introducing some noise, and thus produces the smallest error rates among the three proposed sampling methods. For different decomposition architectures, one-vs-one reduces unclassified regions and in general has higher classification accuracy than the other architectures. By introducing fuzzy logic to bias the penalty parameter, the influence of pooling-induced noise is reduced. Moreover, active learning with different strategies was also tested and shown to improve the accuracy by introducing more representative training samples. Overall, dense arrays employing one-vs-one fuzzy support vector machines with majority-pooling sampling have the smallest average absolute error rate (8.78% for generated phantom maps and 11.5% for reported and transformed phantom map images). The detection accuracy of coarse arrays was found to be significantly lower than for dense array. Conclusions: The results demonstrate the effectiveness of support vector machines using a dense array in detecting refined phantom map shapes, whereas coarse arrays are unsuitable for this task. We therefore propose a two-step approach, using first a non-wearable dense array to detect an accurate phantom map shape, then to apply a wearable coarse stimulation array customized according to the detection results. The proposed methodology can be used as a tool for helping haptic feedback designers and for tracking the evolvement of phantom maps.
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| 3. |
- Huang, Huaiqi, et al.
(författare)
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EMG pattern recognition using decomposition techniques for constructing multiclass classifiers
- 2016
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Ingår i: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, BioRob 2016. - 9781509032877 ; , s. 1296-1301
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Konferensbidrag (refereegranskat)abstract
- To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multiclass classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.
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| 4. |
- Huang, Huaiqi, et al.
(författare)
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Experiment and investigation of two types of vibrotactile devices
- 2016
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Ingår i: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, BioRob 2016. - 9781509032884 - 9781509032877 ; , s. 1266-1271
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Konferensbidrag (refereegranskat)abstract
- Tactile displays have a wide range of applications in assistive technology, prosthetics, and rehabilitation. Vibrotactile stimulation shows potential to be integrated into wearable devices because of its small size, low power consumption, and relative fast responding speed. In this paper, experiments investigating vibrotactile minimal detectable level, just noticeable difference, and two point discrimination distance using eccentric rotating mass (ERM) and linear resonant actuator (LRA) are described and the results were analyzed. LRAs have shown potential to transmit binary information, while ERMs are more suitable for conveying more complexed signals. For both LRAs and ERMs, it is difficult to for subjects to correctly distinguish two points within 45 mm.
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| 5. |
- Huang, Huaiqi, et al.
(författare)
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Multi-modal sensory feedback system for upper limb amputees
- 2017
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Ingår i: Proceedings - 2017 1st New Generation of CAS, NGCAS 2017. - 9781509064472 ; , s. 193-196
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Konferensbidrag (refereegranskat)abstract
- Providing sensory feedback for myoelectric prosthetic users can not only increase the manipulation ability but can also introduce a feeling of embodiment. Previous research has demonstrated the feasibility of providing mechanotactile or vibrotactile feedback on the remaining stumps of amputees. To improve the recognition rate and reduce mental work load when identifying stimulation patterns, we propose a multi-modal sensory feedback system for upper limb amputees. The system consists of five tactile sensors, Bluetooth wireless communication modules, and a custom-designed multi-modal stimulation array incorporating vibrotactile and mechanotactile stimulation modalities. Three amputees (two with phantom map and one without phantom map) tested the system in localization and stimulation intensity identification tasks. Experimental results show the effectiveness of the multi-modal stimulation compared with single modal stimulation. To the best of our knowledge, this is the first attempt to combine vibrotactile and mechanotactile stimulation in a sensory feedback system for upper limb amputees.
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