TY - JOUR
T1 - Inertial Sensor Algorithms to Characterize Turning in Neurological Patients with Turn Hesitations
AU - Shah, Vrutangkumar V.
AU - Curtze, Carolin
AU - Mancini, Martina
AU - Carlson-Kuhta, Patricia
AU - Nutt, John G.
AU - Gomez, Christopher M.
AU - El-Gohary, Mahmoud
AU - Horak, Fay B.
AU - McNames, James
N1 - Funding Information:
Manuscript received September 24, 2020; revised October 26, 2020; accepted November 5, 2020. Date of publication November 12, 2020; date of current version August 20, 2021. The work of Mahmoud El-Gohary, Fay B. Horak, and James McNames was supported by APDM Wearable Technologies, a company that may have a commercial interest in the results of this research and technology. These potential individual conflicts have been reviewed and managed by OHSU. This work was supported in part by the National Institutes of Health under Award R01AG006457 (PI: Horak), the Department of Veterans Affairs Merit Award 5I01RX001075 (PI: Horak), and in part by the Medical Research Foundation of Oregon (PI: Curtze). (Corresponding author: Vrutangkumar V. Shah.) Vrutangkumar V. Shah is with Oregon Health & Science University, Portland, OR 97239 USA (e-mail: shahvr@ohsu.edu). Carolin Curtze is with the University of Nebraska at Omaha, USA.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - Background: One difficulty in turning algorithm design for inertial sensors is detecting two discrete turns in the same direction, close in time. A second difficulty is under-estimation of turn angle due to short-duration hesitations by people with neurological disorders. We aimed to validate and determine the generalizability of a: I. Discrete Turn Algorithm for variable and sequential turns close in time and II: Merged Turn Algorithm for a single turn angle in the presence of hesitations. Methods: We validated the Discrete Turn Algorithm with motion capture in healthy controls (HC, n = 10) performing a spectrum of turn angles. Subsequently, the generalizability of the Discrete Turn Algorithm and associated, Merged Turn Algorithm were tested in people with Parkinson's disease (PD, n = 124), spinocerebellar ataxia (SCA, n = 51), and HC (n = 125). Results: The Discrete Turn Algorithm shows improved agreement with optical motion capture and with known turn angles, compared to our previous algorithm by El-Gohary et al. The Merged Turn algorithm that merges consecutive turns in the same direction with short hesitations resulted in turn angle estimates closer to a fixed 180-degree turn angle in the PD, SCA, and HC subjects compared to our previous turn algorithm. Additional metrics were proposed to capture turn hesitations in PD and SCA. Conclusion: The Discrete Turn Algorithm may be particularly useful to characterize turns when the turn angle is unknown, i.e., during free-living conditions. The Merged Turn algorithm is recommended for clinical tasks in which the single-turn angle is known, especially for patients who hesitate while turning.
AB - Background: One difficulty in turning algorithm design for inertial sensors is detecting two discrete turns in the same direction, close in time. A second difficulty is under-estimation of turn angle due to short-duration hesitations by people with neurological disorders. We aimed to validate and determine the generalizability of a: I. Discrete Turn Algorithm for variable and sequential turns close in time and II: Merged Turn Algorithm for a single turn angle in the presence of hesitations. Methods: We validated the Discrete Turn Algorithm with motion capture in healthy controls (HC, n = 10) performing a spectrum of turn angles. Subsequently, the generalizability of the Discrete Turn Algorithm and associated, Merged Turn Algorithm were tested in people with Parkinson's disease (PD, n = 124), spinocerebellar ataxia (SCA, n = 51), and HC (n = 125). Results: The Discrete Turn Algorithm shows improved agreement with optical motion capture and with known turn angles, compared to our previous algorithm by El-Gohary et al. The Merged Turn algorithm that merges consecutive turns in the same direction with short hesitations resulted in turn angle estimates closer to a fixed 180-degree turn angle in the PD, SCA, and HC subjects compared to our previous turn algorithm. Additional metrics were proposed to capture turn hesitations in PD and SCA. Conclusion: The Discrete Turn Algorithm may be particularly useful to characterize turns when the turn angle is unknown, i.e., during free-living conditions. The Merged Turn algorithm is recommended for clinical tasks in which the single-turn angle is known, especially for patients who hesitate while turning.
KW - Parkinson's disease (PD)
KW - Spinocerebellar ataxia (SCA)
KW - Turning
KW - healthy controls (HC)
KW - inertial sensors
KW - mobility
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U2 - 10.1109/TBME.2020.3037820
DO - 10.1109/TBME.2020.3037820
M3 - Article
C2 - 33180719
AN - SCOPUS:85097150743
VL - 68
SP - 2615
EP - 2625
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 9
M1 - 9258381
ER -