Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment

Abstract

Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment.Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation.Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001).Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

“…Multimodal describes data captured from two or more unique measurement methods. For example, a combination of accelerometer and gyroscope data can be used to detect falls and sit-to-stand transitions 35,36 . Digital tools relying on multimodal data should have evidence of verification available for each sensor, and evidence of analytical validation and clinical validation for the measure itself.…”

On-field player workload exposure and knee injury risk monitoring via deep learning

“…The current generation of wearable sensors are limited by low-fidelity, low resolution, or uni-dimensional data analysis (e.g. velocity) based on gross assumptions of linear regression, which overfit to a simple movement pattern or participant cohort [7], however, researchers have reported success deriving kinematics from these devices for movement classification [40,52]. To improve on these methods, a number of research teams have sought to leverage computer vision and data science techniques, and while initial results appear promising, to date they lack validation to ground truth data, or relevance to specific sporting related tasks [8,49,53].…”

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

“…Instead of quantifying the duration of body positions, it is also common to count the transitions between these positions. The transition between sitting and standing was frequently investigated [ 40 , 41 , 48 , 53 , 57 , 59 , 63 – 73 ], while only three studies detected the transition between lying and sitting [ 48 , 57 , 70 ]. Three of these studies further discriminated between transitions and bending forward [ 53 , 65 , 67 ], and two additional studies specifically detected sit-to-walk transitions since they aimed to compare the timed up and go test with transitions in daily life [ 74 , 75 ].…”

“…Three of these studies further discriminated between transitions and bending forward [ 53 , 65 , 67 ], and two additional studies specifically detected sit-to-walk transitions since they aimed to compare the timed up and go test with transitions in daily life [ 74 , 75 ]. Standing up was further analyzed in terms of speed [ 40 , 63 , 64 , 68 , 71 , 73 – 75 ], range of motion [ 40 , 64 , 71 , 74 , 75 ], and smoothness [ 74 , 75 ]. Only one study detected transfers (i.e., moving from one surface to another without changing body position) [ 62 ].…”

“…The challenge is to discriminate between sit-to-stand and stand-to-sit transitions. This distinction was accomplished by pattern recognition [ 26 , 41 , 53 , 64 , 65 , 67 ], by the orientation of the pelvis after the transition [ 74 , 75 ], by the orientation change of the thigh during the transition [ 66 , 68 , 70 , 72 ], and by estimating the difference in elevation with double integration of the acceleration signal in vertical direction [ 30 , 35 , 56 , 63 , 70 , 73 ] or with a barometric pressure sensor [ 44 , 69 , 71 ]. Lying was often detected via the orientation of the trunk, as described above.…”