Results of Special Neck Exercises in a Patient with Cerebellar Ataxia and Axial Myoclonus Due to ADCK3 Mutation

Menevşe, Özlem; Bilgin, Sevil; Gültekın, Murat

doi:10.4274/tnd.2021.24196

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EMG has been a popular sensor in the detection of ataxiatype disorders, for instance, in the analysis of cerebellar, mitochondrial, and spinocerebellar ataxia [9]- [11], especially focusing on tremors caused by the disease [12] movement disorders in targeted exercises [13], as well as for early detection of the disease in risk groups [14].…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of an Ataxia-type disease from EMG and IMU sensors

Dorszewski

Jiang

Sigg

2022

2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

View full text Add to dashboard Cite

Mitochondrial recessive ataxia syndrome (MIRAS) is a heritable disease, relatively common in Finland. Among other things, patients suffer from ataxia, a movement disorder with difficulties in coordination. To date, no treatment is known for the disease, but medication and therapy can lessen the symptoms, provided that the progression of symptoms is closely monitored to adjust the treatment according to the individual needs. This necessary evaluation is a manual, subjective process.We report about our efforts to explore quantifiable characteristics that could be used to monitor the disease progression objectively using electromyography (EMG) as well as inertial measurement unit (IMU) sensors. In particular, in a study with eight participants, including a patient, we have collected muscle activation as well as IMU data during several tasks. The study found some characteristics that might qualify as indicators of ataxia, such as high-frequency electrical activity (EA) components and similarity of repetitions. We further suggest the use of IMU and machine learning to improve the objective monitoring of the disease's progression.

show abstract

Section: Background and Related Workmentioning

confidence: 99%