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Statistically Sound Human Movement Onset Detection through the Maximal Information Redundancy Criterion
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dijck, Gert Van Hulle, Marc M. Van Vaerenbergh, Jo Van |
| Abstract | patients have a decreased ability in performing activity of daily living (ADL) tasks such as in " drinking a glass of water " , " lifting a bag " , " turning a key " and so on. Sensorimotor force and torque measurements from patients performing these standardized ADL tasks are hypothesized to give quantitative information about the recovery process. Parts of the force/torque measurements which are related to the initiation of the movement in performing ADL tasks contain useful information. Here we address the challenging problem of automatically extracting the movement initiation from these force/torque measurement through machine learning. Therefore we use the statistically sound Maximal Information Redundancy (MIR) criterion. This assumes that movement initiation parts of the signals are characterized by an increased redundancy in the signal. A thorough evaluation of the criterion shows that the accuracy of the criterion in movement onset detection is close to that of manual indication of movement onset by clinical experts. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://gbiomed.kuleuven.be/english/research/50000666/50000669/50488669/neuro_research/neuro_research_mvanhulle/comp_pdf/EMBS2006.pdf |
| Alternate Webpage(s) | https://gbiomed.kuleuven.be/english/research/50000666/50000669/50488669/neuro_research/neuro_research_mvanhulle/comp_pdf/EMBS2006.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
