Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Appl Ergon

Many sensor fusion algorithms for analyzing human motion information collected with inertial measurement units have been reported in the scientific literature. Selecting which algorithm to use can be a challenge for ergonomists that may be unfamiliar with the strengths and limitations of the various options. In this paper, we describe fundamental differences among several algorithms, including differences in sensor fusion approach (e.g., complementary filter vs. Kalman Filter) and gyroscope error modeling (i.e., inclusion or exclusion of gyroscope bias). We then compare different sensor fusion algorithms considering the fundamentals discussed using laboratory-based measurements of upper arm elevation collected under three motion speeds. Results indicate peak displacement errors of <4.5\ub0 with a computationally efficient, non-proprietary complementary filter that did not account for gyroscope bias during each of the one-minute trials. Controlling for gyroscope bias reduced peak displacement errors to <3.0\ub0. The complementary filters were comparable (<1\ub0 peak displacement difference) to the more complex Kalman filters.T42OH008491/ACL/ACL HHSUnited States/T42 OH008491/OH/NIOSH CDC HHSUnited States/T42 OH008436/OH/NIOSH CDC HHSUnited States/T42OH008436/ACL/ACL HHSUnited States/K01 OH011183/OH/NIOSH CDC HHSUnited States/2022-10-26T00:00:00Z32854821PMC960563612055vault:4343

Methodology For Performing Whole Body Pmhs Underbody Blast Impact Testing, And The Corresponding Response Of The Hybrid Iii Dummy And The Finite Element Dummy Model Under Similar Loading Condition

In recent wars, the use of improvised explosive devices and landmines has dramatically increased as a tactical measure to counter armored vehicles. These weapons not only deform and damage the vehicle structure but also produce serious vertical deceleration injuries to mounted occupants. The reported injury patterns largely differ from those in an automotive crash and are often more severe than those in other vertical loading scenarios such as pilot seat ejection, helicopter crash, parachute landing and fall from height. High kinetic energy predominately along the principal vertical (Z-axis) over a short duration makes the underbody blast (UBB) loading conditions unique compared to other vertical and blunt impacts. With the lack of biomechanical response corridors (BRCs), the non-biofidelic nature of the automotive dummies to Z-axis loading and the lack of a finite element dummy model designed for vertical loading make it difficult to evaluate occupant response and develop mitigation strategies for UBB impact conditions. An introduction to the development of the BRCs this study provides a detailed methodology to perform whole body cadaver testing under a laboratory setup. Two whole body PMHS UBB impact tests were conducted using a sled system. An overview of pre-impact parameters such as bone mineral density, instrumentation technique, and vertical impulse generation is presented. Post-test CT scans, response data, and possible injury mechanisms were investigated. In addition, to PMHS testing, the responses of the Hybrid III dummy to short-duration large magnitude vertical acceleration in a laboratory setup were analyzed. Two unique test conditions were investigated using a horizontal sled system to simulate the UBB loading conditions. The biomechanical response in terms of the pelvis acceleration, chest acceleration, lumbar spine force, head accelerations and neck forces were measured during the tests. Subsequently, a series of finite element analyses (FEA) were performed to simulate the physical tests. The material parameters of various components as well as the mesh size were updated based on the high strain rate loading conditions obtained from Zhu et.al (2015) study. The correlation between the Hybrid III test and numerical model was evaluated using the CORA version 3.6.1. The Cora score for WSU FE model was determined to be 0.878 and 0.790 for loading conditions 1 and 2, respectively, in which 1.0 indicated a perfect correlation between the experiment and simulation response. The original LSTC model simulated under the current loading condition became numerically unstable after 12 ms. With repetitive vertical impacts, the Hybrid III dummy pelvis showed a significant increase in the peak acceleration accompanied by rupture of the pelvis foam and flesh. The revised WSU Hybrid III model indicated high stress concentrations at the same location where the pelvis foam and flesh in the actual ATD showed rupture. The stress contour under the ischial tuberosities in the finite element model provides a possible explanation for the material failure in the actual Hybrid III tests

Using inertial measurement units to quantify shoulder elevation after reverse total shoulder arthroplasty: a pilot study comparing goniometric measures captured clinically to inertial measures captured ‘in-the-wild’

Background: Reverse total shoulder arthroplasty (rTSA) is utilized for a variety of indications, but most commonly for patients with rotator cuff arthropathy. This procedure reduces pain, improves satisfaction, and increases clinically measured range of motion (ROM). However, traditional clinical ROM measurements captured via goniometer may not accurately represent ‘real-world’ utilization of ROM. In contrast, inertial measurement units (IMUs) are useful for establishing ROM outside the clinical setting. We sought to measure ‘real-world’ ROM after rTSA using IMUs. Methods: A previously validated IMU-based method for continuously capturing shoulder elevation was used to assess 10 individuals receiving rTSA (1M, 82 ± 5 years) and compared to a previously captured 10 healthy individuals (4M, 69 ± 20 years) without shoulder dysfunction. Control subject data were previously collected over 1 week of continuous use. Patients undergoing rTSA donned sensors for 1 week pre-rTSA, 6 weeks at 3 months post-rTSA following clearance to perform active-independent ROM, and 1 week at 1 year and 2 years post-rTSA. Shoulder elevation was computed continuously each day. Daily continuous elevation was broken into 5° angle ‘bins’ (eg, 0-5°, 5-10°, etc.) and converted to percentage of the total day. IMU-based outcome measures were ROM binned percent (as described previously) and maximum/average elevation each week. Clinical goniometric ROM and patient-reported outcome measures were also captured. Results: No differences existed between patient and healthy control demographics. While patients showed improvement in American Shoulder and Elbow Surgeon (ASES) score, pain score, and goniometric ROM, IMU-based average and maximum elevation were equal between control subjects and patients both pre- and post-rTSA. The percent of time spent above 90° was equal between cohorts pre-rTSA, rose significantly at 3 months post-rTSA, and returned to preoperative levels thereafter. Discussion: Although pain, satisfaction, and ROM measured clinically may improve following rTSA, real-world utilization of improved ROM was not seen herein. Improvements during the acute rehabilitation phase may be transient, indicating longer or more specific rehabilitation protocols are necessary to see chronic improvements in post-rTSA movement patterns

An Analysis of Biomechanical Parameters in OTP Police Physical Intervention Techniques for Occupational Risk Prevention

(1) Background: a set of ergonomic parameters that are relevant for risk assessment methods for the prevention of occupational risks, such as REBA or NIOSH, have been measured by means of inertial sensors that allow capturing the movements of the human body. These methods base their assessment on a number of postural and dynamic parameters. In the case of police physical intervention techniques, trunk, legs, arms, forearms and wrists angles, joint contact force and sheer force at the L5-Pelvic junction, asymmetry (angle and factor), and muscle power are the more relevant parameters to be considered. (2) Method: The data have been collected by means of a motion capture suit equipped with 19 inertial sensors. The large amount of data and the 3-dimensional plots have been managed by a powerful software package specific for ergonomic analysis. The police physical intervention technique used was OTP. (3) Results: Five ergonomic parameters in a traditional police physical intervention technique have been analyzed. REBA scores and ergonomic metrics have been recorded and discussed with some prevention risk limits from the literature. (4) Conclusions: the usage of inertial sensors to capture the movements in OTPs provides a new and quite an efficient viewpoint for occupational risk research studies

Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy