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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
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
IntroductionInertial 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.MethodsIn this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF 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.ResultsA 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 0.002 s [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 0.00 s (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.ConclusionThis cwt-based algorithm for step detection 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.
- Universidade de Lisboa Portugal
- Helmholtz Association of German Research Centres Germany
- Universidade de Lisboa Portugal
- Sapienza University of Rome Italy
- Massachusetts Institute of Technology United States
Neuroscience, accelerometer, gait analysis, home-like activities, older adults, Parkinson’s disease, turning
Neuroscience, accelerometer, gait analysis, home-like activities, older adults, Parkinson’s disease, turning
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1. Hausdorf JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil (2001) 82:1050-6. doi:10.1053/apmr.2001.24893
2. Bloem BR, Hausdorf JM, Visser JE, Giladi N. Falls and freezing of gait in Parkinson's disease: a review of two interconnected, episodic phenomena. Mov Disord (2004) 19:871-84. doi:10.1002/mds.20115
3. Galna B, Lord S, Burn DJ, Rochester L. Progression of gait dysfunction in incident Parkinson's disease: impact of medication and phenotype. Mov Disord (2015) 30:359-67. doi:10.1002/mds.26110
4. van Uem JMT, Marinus J, Canning C, van Lummel R, Dodel R, LiepeltScarfone I, et al. Health-related quality of life in patients with Parkinson's disease-a systematic review based on the ICF model. Neurosci Biobehav Rev (2016) 61:26-34. doi:10.1016/j.neubiorev.2015.11.014
5. Rubenstein LZ, Robbins AS, Schulman BL, Rosado J, Osterweil D, Josephson KR. Falls and instability in the elderly. J Am Geriatr Soc (1988) 36:266-78. doi:10.1111/j.1532-5415.1988.tb01811.x [OpenAIRE]
6. Hamacher D, Singh NB, Van Dieen JH, Heller MO, Taylor WR. Kinematic measures for assessing gait stability in elderly individuals: a systematic review. J R Soc Interface (2011) 8:1682-98. doi:10.1098/rsif. 2011.0416
7. Henderson EJ, Lord SR, Brodie MA, Gaunt DM, Lawrence AD, Close JCT, et al. Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Neurol (2016) 15:249-58. doi:10.1016/S1474-4422(15)00389-0
8. Doi T, Hirata S, Ono R, Tsutsumimoto K, Misu S, Ando H. eTh harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study. J Neuroeng Rehabil (2013) 10:7. doi:10.1186/1743-0003-10-7
9. Pizzigalli L, Filippini A, Ahmaidi S, Jullien H, Rainoldi A. Prevention of falling risk in elderly people: the relevance of muscular strength and symmetry of lower limbs in postural stability. J Strength Cond Res (2011) 25:567-74. doi:10.1519/JSC.0b013e3181d32213 [OpenAIRE]
10. Sejdic E, Lowry KA, Bellanca J, Perera S, Redfern MS, Brach JS. Extraction of stride events from gait accelerometry during treadmill walking. IEEE J Transl Eng Health Med (2016) 4:1-11. doi:10.1109/JTEHM.2015.2504961
- Universidade de Lisboa Portugal
- Helmholtz Association of German Research Centres Germany
- Universidade de Lisboa Portugal
- Sapienza University of Rome Italy
- Massachusetts Institute of Technology United States
- German Center for Neurodegenerative Diseases Germany
- INSTITUTO DE MEDICINA MOLECULAR Portugal
- University of Tübingen Germany
- Kiel University Germany
- Newcastle University United Kingdom
- UNIVERSIDADE DE LISBOA Portugal
- Universidade de Lisboa Portugal
- Research Laboratory of Electronics Massachusetts Institute of Technology United States
- Universidade de Lisboa Portugal
- UNIVERSIDADE DE LISBOA Portugal
- UNIVERSIDADE DE LISBOA Portugal
- UNIVERSIDADE DE LISBOA Portugal
- INSTITUTO DE MEDICINA MOLECULAR JOAO LOBO ANTUNES Portugal
- UNIVERSIDADE DE LISBOA Portugal
- University of Lisbon Portugal
- Newcastle upon Tyne Hospitals NHS Foundation Trust United Kingdom
- UNIVERSIDADE DE LISBOA Portugal
- Robert Bosch Hospital Germany
- Newcastle upon Tyne Hospital NHS Trust United Kingdom
- University of Lisbon Portugal
IntroductionInertial 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.MethodsIn this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF 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.ResultsA 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 0.002 s [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 0.00 s (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.ConclusionThis cwt-based algorithm for step detection 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.