Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson’s Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back - Pham, Minh H.; Elshehabi, Morad; Haertner, Linda; Del Din, Silvia; Srulijes, Karin; Heger, Tanja; Synofzik, Matthis; Hobert, Markus A.; Faber, Gert S.; Hansen, Clint; Salkovic, Dina; Ferreira, Joaquim J.; et al. (Frontiers in Neurology, 2017)
Introduction: Inertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson’s disease (PD) and older adults in both a lab-based and home-like environment. Methods: In this prospective observational study, data was captured from a single 6-degrees of freedom IMU (APDM) (3-DOF accelerometer and 3-DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm. Results: A continuous-wavelet-transform (cwt) based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99%/99% accuracy. Similar results were obtained for TO (99%/100%). In HS detection, Bland-Altman plots showed a mean difference of 0.002s (95% Confidence Interval (CI) -0.09 to 0.10) between the algorithm and the optoelectronic system. The Bland-Altman plot for TO detection showed mean differences of 0.00s (95% CI -0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83%/88% specificity and 88%/89% accuracy. The detection of steps during non-turning phases reached 91%/91% sensitivity, 90%/90% specificity and 91%/91% accuracy. Conclusion: This continuous-wavelet-transform based algorithm for step detection from accelerations measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.