Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
Add more filters










Database
Language
Publication year range
1.
Article in English | MEDLINE | ID: mdl-38271165

ABSTRACT

Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS).


Subject(s)
Mobile Applications , Stroke Rehabilitation , Stroke , Humans , Stroke Rehabilitation/methods , Upper Extremity , Movement , Recovery of Function
3.
J Clin Neurol ; 3(1): 38-44, 2007 Mar.
Article in English | MEDLINE | ID: mdl-19513341

ABSTRACT

BACKGROUND AND PURPOSE: Different mutations in the Cu/Zn superoxide dismutase 1 (SOD1) gene have been reported in approximately 10% of cases of familial amyotrophic lateral sclerosis (ALS). The aim of this study was to analyze for mutations in the SOD1 gene and clinical characteristics in Korean family of ALS. METHODS: A subpopulation of the family reported here has been described previously. In the present study, we analyzed the SOD1 gene in the proband and his immediate family members, who were not reported on previously. Genomic DNA was isolated from the leukocytes of whole blood samples and the coding region of the SOD1 gene was analyzed by PCR and direct sequencing. RESULTS: The genetic alterations were a GGC-to-GTT transition at codon 10 in exon 1 and [IVS4+15_16insA; IVS4+42delG; IVS4+59_60insT] in intron 4. Patients with these mutations exhibit diverse clinical onset symptoms and acceleration of the age at onset in successive generations, which is called anticipation. CONCLUSIONS: We have described a family with familial ALS that showed autosomal-dominant inheritance and two distinct genetic alterations in Cu/Zn-SOD1. The affected family members had different phenotypes and anticipation.

4.
Yonsei Med J ; 44(2): 336-9, 2003 Apr 30.
Article in English | MEDLINE | ID: mdl-12728478

ABSTRACT

The gene responsible for autosomal recessive parkinsonism, parkin, has recently been identified on chromosome 6q. It has been shown to be mutated in Japanese and European families, most of whom had early-onset parkinsonism. Here, we present a family with young-onset parkinsonism of an autosomal recessive inheritance. A homozygous exon 4 deletion in the parkin gene was found in 3 family members. To the best of the authors' knowledge, this is the first report in Korea of familial parkinsonism with the parkin gene mutation.


Subject(s)
Exons , Gene Deletion , Ligases/genetics , Parkinsonian Disorders/genetics , Ubiquitin-Protein Ligases , Female , Genes, Recessive , Humans , Middle Aged
SELECTION OF CITATIONS
SEARCH DETAIL
...