• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.35-40
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• 2011

In this paper, we analyze that co-channel interference mitigation algorithms MMSE (Minimum Mean Square Error), OC (Optimal Combining), ML (Maximum Likelihood) using 2.4Ghz in WBAN (Wireless Body Area Network) system. Also analyze that scenario and channel model by IEEE 802.15.6. ML gives the best performance for all simulation. ML and OC have high complexity than MMSE complexity, because these algorithms should be known channel information of interference users. So these algorithms are difficult to apply to WBAN. Therefore we will study the interference mitigation algorithm that should be accomplished trade-off of between efficiency and complexity.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.111-115
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• 2011

In this paper, we present a novel time-synchronization method for distributed systems to measure the body motion. The distributed system scheme is considered because human data acquisition systems tend to have a centralized controller with sensors connected with a long range of electric wires running through the subject's body, which results in inconvenience. Utilizing simple key switches and digital input ports for reading the key, the proposed method requires a very simple hardware structure, which means less power consumption compared with the well-known ubiquitous sensor network. After measuring the motion data as well as the synchronization pulses, the proposed method compensates, in offline, the difference of the sampling instance between the two systems by scaling the time difference. The paper presents experimental results to show the validity of the proposed method.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.95-101
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• 2011

In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.4
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• pp.245-250
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• 2014

The aims of this study was to investigate influences of speech discrimination score(SDS) depending on inter-syllable pause duration in participant with child of cochlear implantation(CI). 12 child of CI-user participated. The word for SDS was used self-made meaningless three-syllable. The pause duration of inter-syllable was adjusted to 250, 500, 1,000 millisecond(ms). Discrimination score of closed-set speech was obtained at most comfortable loudness(MCL). SDS were improved in CI group for 62.08, 63.75, 69.58 %, but there were no significant changes in child of CI group(p = .4635). SDS was improved depending on inter-syllable pause duration in child of CI.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.285-291
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• 2015

In this paper, we propose a classification algorithm for postures of sitting person by using IMU(inertial measurement unit). This algorithm uses PCA(principle component analysis) for decreasing the number of feature vectors to three and SVM(support vector machine) with RBF(radial basis function) kernel for classifying posture types. In order to collect the data, we designed neckband-shaped earphones with IMU, and applied it to three subjects who are healthy adults. Subjects were experimented three sitting postures, which are neutral posture, smartphoning, and writing. As the result, our PCA-SVM algorithm showed 95% confidence while the dimension of the feature vectors was reduced to 25%.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.1
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• pp.53-60
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• 2015

We conducted a long-term and short-term usability testing on the built-in speech synthesizer of a screen-reader for the people with visual impairments. A total of 20 persons with visual impairments participated in the short-term usability testing, and 10 of them participated in the long-term usability testing. Naturalness and clarity of the synthetic speech were evaluated by MOS scores, preference for various synthetic speeches was examined through a preference test, and the users' satisfaction level and other requirements for the synthetic speech were evaluated by open feedback. We also examined naturalness, clarity, preference, and user requirements for the synthetic speech through a long-term usability testing. Then, we compare and contrast the long-term and short-term usability testing results.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.265-273
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• 2015

Children with autistic spectrum disorder(ASD) have a strength in visual process and systemizing, and they show interest toward things and machines. Therefore, robots have been suggested as a useful tool for the rehabilitation of the children with ASD. A robot can attract children's interest and attention, and it can provide simplified social stimulus. A robot can be applied repetitively, and programmed for the special needs of an individual child. In this study, we review literature related to the use of robots for the rehabilitation of children with ASD. For this purpose, related literature was searched with the keywords of autism and robot. We selected eleven domestic papers, and analyzed their contents to identify robots, stimulus of robots, experiment process and dependent variables.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.29-35
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• 2012

Most Augmentative and Alternative Communication(AAC) use exclusive equipment or studied desktop, tablet PC based windows. Besides, the preceding study offers proper noun dictionary so, henceforward study has problem to innumerable proper noun processing. This paper suggests a method of proper noun processing using a mobile smart equipment. And via the button with virtual keyboard input method and the errors that can occur is also proposing a complementary way. AAC system to check availability for application on Android has been implemented. Experimental results, depending on user location and selection of proper nouns in the around could produce a sentence is derived.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.37-43
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• 2012

We conducted a user-centered usability testing on the Android-based Mobile Augmentative Alternative Communication(AAC) Software. In this paper, we examined functionality, satisfaction, and ease of information searching for a specific function using a task scenario, and we investigated appropriateness of development purposes, contents, instructional strategies, usability, functions of management mode, and user interface of the mobile AAC to the communication needs of children who are nonverbal. We also examined user requirements, preference, satisfaction, and other personal opinions for the mobile AAC using an open feedback. In addition, we investigated users' preference for the size of mobile devices using 4.3", 5.0", and 7.0" mobile devices.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.