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

Inertial sensor-based knee flexion/extension angle estimation

A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth’s magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed

Real-time gait assessment utilizing a new way of accelerometry

Real-time registration of body segment angles is essential in artificial body position control. A new method is presented for the real-time calculation of the lower extremity angles using data obtained from pairs of two one-dimensional accelerometers. It is shown that, assuming rigid-body dynamics and simple hinge joints, relative angles (i.e. angles between segments) can be calculated without integration, thereby solving the problem of integration drift normally associated with accelerometry. During the stance phase of walking, the relative angles can be transformed to absolute angles (i.e. relative to the gravitational field direction) for the different leg segments. The feasibility of relative angle calculation is demonstrated by calculation of the knee angle of a healthy subject. Stability and resolution were demonstrated with measurements during standing. Measurements during standing up, sitting down and walking showed that shock (heel-strike) and skin movements, due to movements of the underlying muscle tissue, are the main error sources. Additional signal processing, e.g. low-pass filtering, can be used to diminish this error. The accuracy of the knee angle found is shown to be high enough to be used in a feedback controller for functional electrostimulation of the lower extremities

Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system

Static and dynamic accuracy of an innovative miniaturized wearable platform for short range distance measurements for human movement applications

Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60°). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0°, the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ±30°, and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ±60°. In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies

Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation

This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p<0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4/spl deg/ (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6/spl deg/ root means square

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

State Estimation for a Humanoid Robot

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in [1] on a quadruped platform by incorporating the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter (EKF) accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. The filter employs a sensor-based prediction model which uses inertial data from an IMU and corrects for integrated error using a kinematics-based measurement model which relies on joint encoders and a kinematic model to determine the relative position and orientation of the feet. A nonlinear observability analysis is performed on both the original and updated filters and it is concluded that the new filter significantly simplifies singular cases and improves the observability characteristics of the system. Results on simulated walking and squatting datasets demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on Intelligent Robots and Systems (2014) 952-95