Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson's Disease.
Teodora MaticSomayeh AghanavesiMevludin MemediDag NyholmFilip BergquistVida GroznikJure ZabkarAleksander SadikovPublished in: AIME (2019)
Keyphrases
- sensor data
- unsupervised learning
- sensor networks
- multiple sclerosis
- sensor measurements
- medical doctors
- early diagnosis
- patient groups
- data streams
- supervised learning
- cardiovascular disease
- disease progression
- chronic obstructive pulmonary disease
- image sequences
- clinical studies
- health monitoring
- amyotrophic lateral sclerosis
- stream data
- sensor readings
- human activities
- sensor data streams
- wireless sensor networks
- smart environments
- model selection
- moving objects
- wearable sensors
- deep brain stimulation
- heterogeneous sensor networks
- low cost sensors
- machine learning
- earth observation
- chronic disease
- dimensionality reduction
- multiple sensors
- data fusion
- raw sensor data
- semi supervised
- feature selection
- data sets