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Application of wavelet scattering networks in classification of ultrasound image sequences

Published on Sep 1, 2017
· DOI :10.1109/ULTSYM.2017.8091649
Amir Khan7
Estimated H-index: 7
(GMU: George Mason University),
Ananya S. Dhawan (GMU: George Mason University)+ 2 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
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Abstract
Recently, ultrasound imaging of muscle contractions has been used by several research groups to infer volitional motor intent of the user, and has shown promise as a novel muscle computer interface. Learning spatiotemporal features from ultrasound image sequences is challenging because of deformations introduced by probe repositioning. The image features are sensitive to probe placement and even small displacements during cross-session donning and doffing of the probe could compromise the classification accuracy when using a model trained on a previous session. This requires frequent recalibration. Deep learning based feature extractors have been shown to be invariant to translation, rotation and slight deformations. In this study, we investigated the feasibility of wavelet-based deep scattering features to preserve motion classification accuracy across multiple donning and doffing sessions. It was demonstrated that scattering features, which do not require learning filter weights, can significantly improve the cross-session classification accuracy by 20–30%. It can be concluded that these features are robust to minor probe displacements. They need to be further investigated with a larger data set to investigate their robustness in accurately classifying different muscle movements.
  • References (13)
  • Citations (0)
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References13
Newest
Published on Aug 1, 2017in IEEE Transactions on Human-Machine Systems 3.33
Weichao Guo4
Estimated H-index: 4
(SJTU: Shanghai Jiao Tong University),
Xinjun Sheng15
Estimated H-index: 15
(SJTU: Shanghai Jiao Tong University)
+ 1 AuthorsXiangyang Zhu21
Estimated H-index: 21
(SJTU: Shanghai Jiao Tong University)
Advanced myoelectric prosthetic hands are currently limited due to the lack of sufficient signal sources on amputation residual muscles and inadequate real-time control performance. This paper presents a novel human–machine interface for prosthetic manipulation that combines the advantages of surface electromyography (EMG) and near-infrared spectroscopy (NIRS) to overcome the limitations of myoelectric control. Experiments including 13 able-bodied and three amputee subjects were carried out to e...
Published on May 2, 2017 in CHI (Human Factors in Computing Systems)
Jess McIntosh5
Estimated H-index: 5
(UoB: University of Bristol),
Asier Marzo10
Estimated H-index: 10
(UoB: University of Bristol)
+ 1 AuthorsCarol Phillips1
Estimated H-index: 1
(University Hospitals Bristol NHS Foundation Trust)
Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a f...
Published on Jan 1, 2017in IEEE Access 4.10
Muhammad Ahmad4
Estimated H-index: 4
(National University of Sciences and Technology),
Awais M. Kamboh7
Estimated H-index: 7
(National University of Sciences and Technology)
+ 1 AuthorsAmir Khan7
Estimated H-index: 7
(National University of Sciences and Technology)
Epilepsy is one of the most common neurological disorders, which manifests as unprovoked seizures. The prevalence of epilepsy is higher in developing countries, where medical facilities are ill-equipped and under-staffed. Mobile EEG devices promise a new dawn for long-term ambulatory EEG monitoring, which has a potential to revolutionize health care for neurological disorders especially epilepsy. Increasing the outreach to underserved communities and continuous monitoring of patients will yield ...
Published on Aug 1, 2016in IEEE Transactions on Biomedical Engineering 4.49
Nima Akhlaghi3
Estimated H-index: 3
(GMU: George Mason University),
Clayton A. Baker2
Estimated H-index: 2
(GMU: George Mason University)
+ 7 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle–computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust gr...
Published on Mar 1, 2015in IEEE Transactions on Image Processing 6.79
Kuang-Yu Chang7
Estimated H-index: 7
(AS: Academia Sinica),
Chu-Song Chen29
Estimated H-index: 29
(AS: Academia Sinica)
This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual b...
Siddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University),
Huzefa Rangwala18
Estimated H-index: 18
(GMU: George Mason University)
+ 5 AuthorsJoseph J. Pancrazio15
Estimated H-index: 15
(GMU: George Mason University)
Recently there have been major advances in the electro-mechanical design of upper extremity prosthetics. However, the development of control strategies for such prosthetics has lagged significantly behind. Conventional noninvasive myoelectric control strategies rely on the amplitude of electromyography (EMG) signals from flexor and extensor muscles in the forearm. Surface EMG has limited specificity for deep contiguous muscles because of cross talk and cannot reliably differentiate between indiv...
Claudio Castellini26
Estimated H-index: 26
,
Georg Passig8
Estimated H-index: 8
,
Emanuel Zarka1
Estimated H-index: 1
(DLR: German Aerospace Center)
Medical ultrasound imaging is a well-known technique to gather live views of the interior of the human body. It is totally safe, it provides high spatial and temporal resolution, and it is nowadays available at any hospital. This suggests that it could be used as a human-computer interface. In this paper, we use ultrasound images of the human forearm to predict the finger positions, including thumb adduction and thumb rotation. Our experimental results show that there is a clear linear relations...
Published on Oct 4, 2009 in UIST (User Interface Software and Technology)
T. Scott Saponas21
Estimated H-index: 21
(UW: University of Washington),
Desney S. Tan50
Estimated H-index: 50
(Microsoft)
+ 3 AuthorsJames A. Landay60
Estimated H-index: 60
(UW: University of Washington)
Previous work has demonstrated the viability of applying offline analysis to interpret forearm electromyography (EMG) and classify finger gestures on a physical surface. We extend those results to bring us closer to using muscle-computer interfaces for always-available input in real-world applications. We leverage existing taxonomies of natural human grips to develop a gesture set covering interaction in free space even when hands are busy with other objects. We present a system that classifies ...
Published on Feb 16, 2009in Biological Cybernetics 1.30
Claudio Castellini26
Estimated H-index: 26
,
Patrick van der Smagt17
Estimated H-index: 17
(DLR: German Aerospace Center)
One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine lear...
Published on Aug 1, 2006 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Ramana Vinjamuri10
Estimated H-index: 10
(University of Pittsburgh),
Zhi-Hong Mao22
Estimated H-index: 22
(University of Pittsburgh)
+ 1 AuthorsMingui Sun24
Estimated H-index: 24
(University of Pittsburgh)
This paper explores the limitations of sEMG (surface Electromyography) signals collected from the extrinsic muscles in the forearm in predicting the postures of human hand. Four subjects were asked to try ten extreme postures of hand which need high effort. Two of these four subjects were asked to try ten more normal postures which did not need effort. During the experiments, muscle activity and static postures of the hand were measured. The data obtained were analyzed by principal component ana...
Cited By0
Newest
Published on Dec 1, 2019in Scientific Reports 4.01
Ananya S. Dhawan (GMU: George Mason University), Biswarup Mukherjee3
Estimated H-index: 3
(GMU: George Mason University)
+ 7 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographic control. Unlike myoelec...
Published in bioRxiv
Ananya S. Dhawan (GMU: George Mason University), Biswarup Mukherjee3
Estimated H-index: 3
(GMU: George Mason University)
+ -3 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
Technological advances in multi-articulated prosthetic hands have outpaced the methods available to amputees to intuitively control these devices. Amputees often cite difficulty of use as a key contributing factor for abandoning their prosthesis, creating a pressing need for improved control technology. A major challenge of traditional myoelectric control strategies using surface electromyography electrodes has been the difficulty in achieving intuitive and robust proportional control of multipl...