Towards holistic free-living assessment in Parkinson's disease: Unification of gait and fall algorithms with a single accelerometer
- Resource Type
- Conference
- Authors
- Godfrey, Alan; Bourke, Alan; Del Din, Silvia; Morris, Rosie; Hickey, Aodhan; Helbostad, Jorunn L; Rochester, Lynn
- Source
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. :651-654 Aug, 2016
- Subject
- Bioengineering
Algorithm design and analysis
Legged locomotion
Acceleration
Accelerometers
Diseases
Biomedical monitoring
Pragmatics
- Language
- ISSN
- 1557-170X
1558-4615
Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm. We tested the unified algorithms on a cohort of young healthy adults within a laboratory. We then deployed the algorithms on longitudinal (7 day) accelerometer-based data from an older adult with Parkinson's disease (PD) to quantify real world gait and falls. We compared instrumented falls to a self-reported falls diary to test algorithm efficiency and discuss the use of unified algorithms to impact free-living assessment in PD where accurate recognition of gait may reduce the number of automated detected falls (38/week). This informs ongoing work to use gait and related outcomes as pragmatic clinical markers.