Wavelet-based algorithm for auto-detection of daily living activities of older adults captured by multiple inertial measurement units (IMUs)

Abstract

A recent trend in human motion capture is the use of inertial measurement units (IMUs) for monitoring and performance evaluation of mobility in the natural living environment. Although the use of such systems have grown significantly, the development of methods and algorithms to process IMU data for clinical purposes is still limited. The aim of this work is to develop algorithms based on wavelet transform and discrete-time detection of events for the automatic segmentation of tasks related activities of daily living (ADL) from body worn IMUs. Seven healthy older adults (73 ± 4 years old) performed 10 ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5 min). They wore a suit (Synertial UK Ltd IGS-180) comprised of 17 IMUs positioned strategically on body segments to capture full body motion. The proposed method automatically detected the number of template waveforms (representing each movement separately) using discrete wavelet transform (DWT) and discrete-time detection of events based on angular velocity, linear acceleration and 3D orientation data of pertinent IMUs. The sensitivity (Se.) and specificity (Sp.) of detection for the proposed method was established using time stamps of10tasks obtained from visual segmentation of each trial using the video records and the avatar provided by the system's software. At first, we identified six pertinent sensors that were strongly associated to different activities (at most two sensors/task) that allowed detection of tasks with high accuracy. The proposed algorithm exhibited significant global accuracy (N events = 1999, Se. = 97.5%, Sp. = 94%), despite the variation in the occurrences of the performed tasks (free living). The Se. varied from 94% to 100% for all the detected ADL tasks and Sp. ranged from 90% to 100% with the worst Sp. = 85 and 87% for Release_mid (reaching for object held just beyond reach at chest height) and Turning_Left tasks, respectively. This study demonstrated that DWT in conjunction with a nonlinear transform and auto-adaptive thresholding process for decision rules are highly efficient in detecting and segmenting tasks performed during free-living activities. This study also helped to determine the optimal number of sensors, and their location to detect such activities. This work lays the foundation for the automatic assessment of mobility performance within the segmented signals, as well as potentially helps differentiate populations based on their mobility patterns and symptomatology.

Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors

“…In some patients, shuffling gaits minimized the angular velocity ( ω y ) of the hip; therefore it degraded the signal-to-noise ratio in the sacrum IMU dropping the signal below the normalized threshold. Previously, we have used the shin IMU to detect walking in simulated free-living environment [14, 15] with an adaptive thresholding to mitigate the gait variability among participants. However, using the shin IMU alone was not sufficient in isolating walking due to extraneous movements that participants with PD might initiate during the TUG.…”

“…We recently proposed detection and segmentation algorithms based on peak detection of IMUs data to automatically isolate common activities in daily living in healthy older adults [14, 15, 33]. Using multiple IMUs positioned on different limb segments on the body, we were able to accurately segment and detect activities of daily living in a homogenous healthy aging population using kinematics and orientation data obtained from these IMUs.…”

Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

“…It also demonstrates the power of orientation data assessed with AHRS. The full potential of such an approach will only be reached when combined with automatic recognition and segmentation of activities (Nguyen et al, 2015 ; Ayachi et al, 2016a , b ). Additionally, this work also shows that the sigma-lognormal model can be used to fit the cranio-caudal signature.…”

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography

“…In the signal analysis, such slowly changing signals are considered low frequency while dramatically changing signals are considered high frequency. Wavelet transformation is an effective tool for detecting the discontinuity points [8, 27]. The detailed decomposition coefficient at discontinuity points is rather high while the other coefficients spread around zero [28].…”

“…Therefore, the reconstructed detail coefficient would have higher amplitude at the discontinuity point, which is also the first point corresponding to disconnected EVCs. Therefore, we can localize the group of EVCs corresponding to disconnected electrodes by identifying the frequency discontinuity point in the ascending EVC sequence [27]. In our study, we used db4 wavelet function to perform the wavelet transform and to localize the discontinuity by detecting the minimum value.…”