Inertial Sensing in Biomechanics

2011 ◽  
pp. 70-100 ◽  
Author(s):  
Angelo M. Sabatini
Keyword(s):  
Independent Living ◽  
Inertial Sensor ◽  
Ambulatory Monitoring ◽  
Human Motion ◽  
Computational Techniques ◽  
Navigation Systems ◽  
Inertial Sensing ◽  
Current Location ◽  
Specialized Equipment ◽  
Physiological Sensors

Sensing approaches for ambulatory monitoring of human motion are necessary in order to objectively determine a person’s level of functional ability in independent living. Because this capability is beyond the grasp of the specialized equipment available in most motion analysis laboratories, body-mounted inertial sensing has been receiving increasing interest in the biomedical domain. Crucial to the success of this certainly not new sensing approach will be the capability of wearable inertial sensor networks to accurately recognize the type of activity performed (context awareness) and to determine the person’s current location (personal navigation), eventually in combination with other biomechanical or physiological sensors — key requirements in applications of wearable and mobile computing as well. This chapter reviews sensor configurations and computational techniques that have been implemented or are considered to meet the converging requirements of a wealth of application products, including ambulatory monitors for automatic recognition of activity, quantitative analysis of motor performance, and personal navigation systems.

Inertial Sensing in Biomechanics

2011 ◽  
pp. 906-934
Author(s):  
Angelo M. Sabatini
Keyword(s):  
Independent Living ◽  
Inertial Sensor ◽  
Ambulatory Monitoring ◽  
Human Motion ◽  
Computational Techniques ◽  
Navigation Systems ◽  
Inertial Sensing ◽  
Current Location ◽  
Specialized Equipment ◽  
Physiological Sensors

Sensing approaches for ambulatory monitoring of human motion are necessary in order to objectively determine a person’s level of functional ability in independent living. Because this capability is beyond the grasp of the specialized equipment available in most motion analysis laboratories, body-mounted inertial sensing has been receiving increasing interest in the biomedical domain. Crucial to the success of this certainly not new sensing approach will be the capability of wearable inertial sensor networks to accurately recognize the type of activity performed (context awareness) and to determine the person’s current location (personal navigation), eventually in combination with other biomechanical or physiological sensors — key requirements in applications of wearable and mobile computing as well. This chapter reviews sensor configurations and computational techniques that have been implemented or are considered to meet the converging requirements of a wealth of application products, including ambulatory monitors for automatic recognition of activity, quantitative analysis of motor performance, and personal navigation systems.

scholarly journals A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun
Keyword(s):  
Magnetic Measurements ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Human Motion ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Hybrid Framework ◽  
Outdoor Environments ◽  
Adaptive Fusion ◽  
Indoor And Outdoor Environments

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

scholarly journals S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

10.2196/13961 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13961
Author(s):  
Kim Sarah Sczuka ◽  
Lars Schwickert ◽  
Clemens Becker ◽  
Jochen Klenk
Keyword(s):  
Real World ◽  
Independent Living ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Center Of Mass ◽  
Fall Detection ◽  
Video Data ◽  
Sensor Data ◽  
Movement Behavior ◽  
The Real

Background Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person’s center of mass during fall events based on the available sensor information. Conclusions Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.

A Universal Approach for Processing any MEMS Inertial Sensor Configuration for Land-Vehicle Navigation

2007 ◽  
Vol 60 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Xiaoji Niu ◽  
Sameh Nasser ◽  
Chris Goodall ◽  
Naser El-Sheimy
Keyword(s):  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Micro Electro Mechanical Systems ◽  
Land Vehicle ◽  
Mems Sensor ◽  
Navigation Algorithm ◽  
Main Challenge ◽  
Universal Approach ◽  
Sensor Configuration

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

scholarly journals Single-Element Dual-Interferometer for Precision Inertial Sensing

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4986
Author(s):  
Yichao Yang ◽  
Kohei Yamamoto ◽  
Victor Huarcaya ◽  
Christoph Vorndamme ◽  
Daniel Penkert ◽  
...  
Keyword(s):  
High Precision ◽  
Degrees Of Freedom ◽  
Dynamic Range ◽  
Inertial Sensor ◽  
Single Element ◽  
Inertial Sensing ◽  
Optical Readout ◽  
One Step ◽  
Isolation Systems ◽  
Laser Interferometers

Tracking moving masses in several degrees of freedom with high precision and large dynamic range is a central aspect in many current and future gravitational physics experiments. Laser interferometers have been established as one of the tools of choice for such measurement schemes. Using sinusoidal phase modulation homodyne interferometry allows a drastic reduction of the complexity of the optical setup, a key limitation of multi-channel interferometry. By shifting the complexity of the setup to the signal processing stage, these methods enable devices with a size and weight not feasible using conventional techniques. In this paper we present the design of a novel sensor topology based on deep frequency modulation interferometry: the self-referenced single-element dual-interferometer (SEDI) inertial sensor, which takes simplification one step further by accommodating two interferometers in one optic. Using a combination of computer models and analytical methods we show that an inertial sensor with sub-picometer precision for frequencies above 10 mHz, in a package of a few cubic inches, seems feasible with our approach. Moreover we show that by combining two of these devices it is possible to reach sub-picometer precision down to 2 mHz. In combination with the given compactness, this makes the SEDI sensor a promising approach for applications in high precision inertial sensing for both next-generation space-based gravity missions employing drag-free control, and ground-based experiments employing inertial isolation systems with optical readout.

scholarly journals Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks

2016 ◽  
Vol 2 (1) ◽  
pp. 711-714 ◽  
Author(s):  
Daniel Laidig ◽  
Sebastian Trimpe ◽  
Thomas Seel
Keyword(s):  
Joint Angle ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Large Set ◽  
Fusion Algorithm ◽  
The Cost ◽  
Realtime Measurement ◽  
Event Based

AbstractWe examine the usefulness of event-based sampling approaches for reducing communication in inertial-sensor-based analysis of human motion. To this end we consider realtime measurement of the knee joint angle during walking, employing a recently developed sensor fusion algorithm. We simulate the effects of different event-based sampling methods on a large set of experimental data with ground truth obtained from an external motion capture system. This results in a reduced wireless communication load at the cost of a slightly increased error in the calculated angles. The proposed methods are compared in terms of best balance of these two aspects. We show that the transmitted data can be reduced by 66% while maintaining the same level of accuracy.

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