Activity patterns in stroke patients - is there a trend in behaviour during rehabilitation?. Derungs, A., Seiter, J., Schuster-Amft, C., & Amft, O. In HBU 2015: Proceedings of the 6th International Workshop on Human Behaviour Understanding, of Lecture Notes in Computer Science, In press. Springer. abstract bibtex We describe stroke patients' activity patterns and trends based on motion data acquired during their stay in an ambulatory daycare centre. Our aim was to explore and quantify intensity and development in the patients' activity patterns as these may change during the rehabilitation process. We analyse motion data recordings from wearable inertial measurement units of eleven patients up to eleven days, totally 102 recording days. Using logic rules, we extract activity primitives, including affected arm move, sit, stand, walking, etc. from selected channels of the continuous median-filtered sensor data. Using relative duration of the activity primitives, we examine patient activity patterns regarding independence in mobility, distribution of walking over the days and trends in using the affected body side. Due to the heterogeneity of patients' behaviour, we focused on analysing patient-specific activity patterns. Our exploration showed that the rule-based activity primitive analysis is beneficial to understand individual patient activity.
@InProceedings{Derungs2015-P_HBU,
Title = {Activity patterns in stroke patients - is there a trend in behaviour during rehabilitation?},
Author = {Adrian Derungs and Julia Seiter and Corina Schuster-Amft and Oliver Amft},
Booktitle = {HBU 2015: Proceedings of the 6th International Workshop on Human Behaviour Understanding},
Year = {In press},
Publisher = {Springer},
Series = {Lecture Notes in Computer Science},
Abstract = {We describe stroke patients' activity patterns and trends based on motion data acquired during their stay in an ambulatory daycare centre. Our aim was to explore and quantify intensity and development in the patients' activity patterns as these may change during the rehabilitation process. We analyse motion data recordings from wearable inertial measurement units of eleven patients up to eleven days, totally 102 recording days. Using logic rules, we extract activity primitives, including affected arm move, sit, stand, walking, etc. from selected channels of the continuous median-filtered sensor data. Using relative duration of the activity primitives, we examine patient activity patterns regarding independence in mobility, distribution of walking over the days and trends in using the affected body side. Due to the heterogeneity of patients' behaviour, we focused on analysing patient-specific activity patterns. Our exploration showed that the rule-based activity primitive analysis is beneficial to understand individual patient activity.},
File = {Derungs2015-P_HBU.pdf:Derungs2015-P_HBU.pdf:PDF},
Keywords = {Activity primitives, trend indication, exploratory behaviour description, rule base data extraction},
Owner = {oamft},
Timestamp = {2015/08/21}
}
Downloads: 0
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