TY - GEN
T1 - Detection of an Ataxia-type disease from EMG and IMU sensors
AU - Dorszewski, Tobias
AU - Jiang, Weixuan
AU - Sigg, Stephan
N1 - Funding Information:
We acknowledge partial funding by the German Academic Exchange Service (DAAD) in the frame of the DAAD RISE Program.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/5/6
Y1 - 2022/5/6
N2 - 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.
AB - 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.
KW - Ataxia
KW - IMU
KW - MIRAS
KW - sEMG
UR - http://www.scopus.com/inward/record.url?scp=85130592209&partnerID=8YFLogxK
U2 - 10.1109/PerComWorkshops53856.2022.9767235
DO - 10.1109/PerComWorkshops53856.2022.9767235
M3 - Conference contribution
AN - SCOPUS:85130592209
T3 - 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022
SP - 712
EP - 717
BT - 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022
PB - IEEE
T2 - IEEE International Conference on Pervasive Computing and Communications Workshops
Y2 - 21 March 2022 through 25 March 2022
ER -