Inertial Measurement Unit Sensor Research Articles

Low-Cost Real-Time Localisation for Agricultural Robots in Unstructured Farm Environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

Using accelerometers to identify a high risk of catastrophic musculoskeletal injury in three racing Thoroughbreds.

To describe the process whereby the screening of racing Thoroughbreds with accelerometer-based inertial measurement unit (IMU) sensors followed by clinical evaluation and advanced imaging identified potentially catastrophic musculoskeletal injuries in 3 horses. 3 Thoroughbred racehorses. All cases demonstrated an abnormal stride pattern either during racing (cases 1 and 2) or while breezing (case 3) and were identified as being at very high risk of catastrophic musculoskeletal injury by an algorithm derived from IMU sensor files from > 20,000 horses' race starts. Veterinary examination and 18F-sodium fluoride (18F-NaF) positron emission tomography were performed within 10 days of the respective race or breeze in each of the cases. The intensity and location of the 18F-NaF uptake in the condyles of the third metacarpal bone in cases 1 and 2 identified them as at potential increased risk of condylar fracture. The pattern and intensity of the 18F-NaF uptake in case 3 indicated that the third carpal bone was likely responsible for the horse's lameness, with an impending slab fracture subsequently identified on radiographs. Following periods of convalescence, cases 1 and 2 returned to racing and were identified by the sensor system as no longer being at high risk of catastrophic musculoskeletal injury. Case 3 returned to training but has yet to return to racing. When worn by Thoroughbreds while racing or breezing, these IMU sensors can identify horses at high risk of catastrophic musculoskeletal injury, allowing for veterinary intervention and the potential avoidance of such injuries.

Energy transfer during tree movement for different wind conditions and forest configurations

Catastrophic winds from tropical cyclones and storms cause tree failure in forests, leading to economic and environmental losses. Tree failure, especially uprooting, results from energy transfer from winds to the roots through the tree stem. However, we do not fully understand how trees in forests respond to winds under different weather conditions and whether the responses remain unchanged with the configurations of the forest. In this study, we established two Cryptomeria japonica plots, namely P-100 as a control (3000 stem ha−1), and P-50, which was thinned to halve the control stand density (1500 stem ha−1) in November 2017, in Kasumigaura City, Japan. Two kinds of sensors were installed on the tree stems in the plots and data was collected over the following 2 years. Strain gauge transducers at the stem base were used to calculate turning moments. Inertial measurement unit (IMU) sensors at 6 m height provided the x, y, and z coordinates of the tree motion. At the end of the observation period, we conducted tree-pulling experiments to calculate the maximum turning moments the trees could resist. During the observation period, the trees experienced winds from typhoons, including super typhoon Trami in 2018, causing some tree failure in the thinned plot (P-50). Based on this damage incident, we selected four different forest configurations and 18 windy conditions (7 typhoons and 11 weather conditions in which a maximum wind speed of more than 7 m s−1 was observed). We found two dominant movement frequencies, one at between 0.2 – 0.5 Hz and one at between 2.0 – 2.3 Hz. The higher movement frequency appeared at the lower wind speeds and the lower movement frequency appeared at the higher wind speeds. The transition wind speeds from the higher to the lower movement frequency were found between 1.79 and 7.44 m s–1 in the P-100 unthinned plot and between 1.57 and 5.63 m s–1 in the P-50 thinned plot. In addition, we determined that the resistance to the damage over a 10-min period during typhoon Trami, based on the tree that uprooted, was only 48 % of the maximum tree resistance estimated from the tree-pulling experiments. Our study describing tree dynamic responses to winds under different forest configurations and wind conditions could provide alternative methods for modifying the forest environment, especially through forest management activities such as thinning and harvesting, in order to mitigate wind damage risk in the future.

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers

Motion analysis has played a crucial role in providing gait analysis for prosthetic users. Understanding kinematics in motion analysis allows for the evaluation of the effects of prostheses and the development of a prosthetic component design that aids in walking within the community. However, there are currently limited open datasets available to study the locomotion of individuals using transtibial prostheses, and most research studies involve a limited number of participants. This dataset shows 30 transtibial prosthesis users walking comfortably on a 10-meter walkway inside a laboratory. We offer a set of joint ankles obtained from the inertial measurement unit (IMU) signals in CSV file format. We also provide the optical motion capture (OMC) system’s raw trajectory marker data in C3D file format and joint angle in CSV file format. This open dataset will provide resources for professionals interested in amputee gait for research in amputee gait detection and tracking algorithms. Moreover, the data will be the control data for comparison in developing advanced prosthetic components.

HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications

Wearable cameras provide an objective method to visually confirm and automate the detection of health-risk behaviors such as smoking and overeating, which is critical for developing and testing adaptive treatment interventions. Despite the potential of wearable camera systems, adoption is hindered by inadequate clinician input in the design, user privacy concerns, and user burden. To address these barriers, we introduced HabitSense, an open-source1, multi-modal neck-worn platform developed with input from focus groups with clinicians (N=36) and user feedback from in-wild studies involving 105 participants over 35 days. Optimized for monitoring health-risk behaviors, the platform utilizes RGB, thermal, and inertial measurement unit sensors to detect eating and smoking events in real time. In a 7-day study involving 15 participants, HabitSense recorded 768 hours of footage, capturing 420.91 minutes of hand-to-mouth gestures associated with eating and smoking data crucial for training machine learning models, achieving a 92% F1-score in gesture recognition. To address privacy concerns, the platform records only during likely health-risk behavior events using SECURE, a smart activation algorithm. Additionally, HabitSense employs on-device obfuscation algorithms that selectively obfuscate the background during recording, maintaining individual privacy while leaving gestures related to health-risk behaviors unobfuscated. Our implementation of SECURE has resulted in a 48% reduction in storage needs and a 30% increase in battery life. This paper highlights the critical roles of clinician feedback, extensive field testing, and privacy-enhancing algorithms in developing an unobtrusive, lightweight, and reproducible wearable system that is both feasible and acceptable for monitoring health-risk behaviors in real-world settings.

Dynamic Soft Tissue Artifacts during Impulsive Loads: Measurement Errors Vary With Wearable Inertial Measurement Unit Sensor Design.

Characterize and model Inertial Measurement Unit (IMU) errors due to transient dynamic soft tissue artifacts excited by impulsive loads, such as foot strikes during running and jumping. We instrumented 10 participants (5 female, 5 male) with IMUs on the dominant leg. An ankle IMU measured reference vertical accelerations during impulsive loads and was cross-validated against vertical force measures. Two IMUs on the posterior shank and anterior shank were used to characterize errors caused by dynamic soft tissue artifacts with respect to the reference. Shank sensors' masses were varied to explore their effect on dynamic soft tissue artifacts. Both the posterior IMU and anterior IMU overestimated peak vertical accelerations during the impulsive load (gain of 2.18 ± 0.63 and 1.55 ± 0.35 respectively). The post- impulsive load oscillation duration and natural frequency varied with sensor mass according to an underdamped second-order system, with posterior IMU and anterior IMU durations of 326 ± 75ms and 151 ± 50ms respectively and natural frequencies of 9.79 ± 2.68Hz and 18.22 ± 12.10Hz respectively. Low-pass filtering reduced overestimation of peak vertical accelerations, but also attenuated the reference measure. Our study suggests dynamic soft tissue artifacts result in transient, but substantial measurement errors that may not be appropriately mitigated through low-pass filtering. However, these dynamic soft tissue artifacts can be modeled using an underdamped second-order system and used to estimate material properties of underlying soft tissue. We demonstrate that dynamic soft tissue artifacts can be modeled and potentially mitigated to improve accuracy in applications necessitating measurement of impulsive loads such as foot strikes.

The NACOB multi-surface walking dataset

Walking is a fundamental aspect of human movement, and understanding how irregular surfaces impact gait is crucial. Existing gait research often relies on laboratory settings with ideal surfaces, limiting the applicability of findings to real-world scenarios. While some irregular surface datasets exist, they are often small or lack biomechanical gait data. In this paper, we introduce a new irregular surface dataset with 134 participants walking on surfaces of varying irregularity, equipped with inertial measurement unit (IMU) sensors on the trunk and lower right limb (foot, shank, and thigh). Collected during the North American Congress on Biomechanics conference in 2022, the dataset aims to provide a valuable resource for studying biomechanical adaptations to irregular surfaces. We provide the detailed experimental protocol, as well as a technical validation in which we developed a machine learning model to predict the walking surface. The resulting model achieved an accuracy score of 95.8%, demonstrating the discriminating biomechanical characteristics of the dataset’s irregular surface gait data.

Development of an IMU based 2-segment foot model for an applicable medical gait analysis

BackgroundThe two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC.Research questionCan an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics?MethodsTo establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping.ResultsThe 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort’s norm data set showed a homogeneous motion pattern for gait.ConclusionThe 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations.Level of evidenceLevel II.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

Using IMU sensors to compare rowing ergometers with rowing on the water

Indoor rowing ergometers serve as tools to refine stroke technique, improve stamina, and allow training regardless of weather conditions. Currently, the most widely utilized models are the Concept 2 (C2) and the Rowperfect 3 (RP3) static and dynamic ergometers. The timing and magnitude of acceleration during different phases of the rowing stroke have been shown to play a crucial role in performance. Current methods used to extract individual rowing strokes are difficult to apply and utilize complicated measurements, such as oar angle, filters, or machine learning. This paper compares the rowing acceleration profile across three rowing training devices. The C2 and RP3 ergometer handle and seat accelerations are compared to those of a single scull shell using a peak detection signal processing technique. This technique easily identifies peaks during a rowing session so that individual strokes can be extracted. Inertial measurement units (IMU) were attached to the oar handle and the ergometer seat/cage and the scull to measure the acceleration profile of individual strokes. The ergometer drag factor was adjusted to match the boat’s drag factor. One Division I male athlete rowed for 90 s, with 60 s of steady state rowing at 20 strokes/min (spm). The mean difference in acceleration between each ergometer and the boat was calculated. Findings suggest the RP3 ergometer acceleration profile more closely matches that of on-water rowing. Our analysis illuminates key differences between ergometers and on-water rowing, which can help rowers understand how their ergometer training translates to on-water rowing.

Automated Detection of In-Home Activities with Ultra-Wideband Sensors.

As Canada's population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.

Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps.

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.

On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates.

Using artificial intelligence algorithms to approximate data from inertial measurement unit sensors and strain gauges in basketball players

Using artificial intelligence algorithms to approximate data from inertial measurement unit sensors and strain gauges in basketball players

Validity of Inertial Measurement Unit (IMU Sensor) for Measurement of Cervical Spine Motion, Compared with Eight Optoelectronic 3D Cameras Under Spinal Immobilization Devices.

The assessment of cervical spine motion is critical for out-of-hospital patients who suffer traumatic spinal cord injuries, given the profound implications such injuries have on individual well-being and broader public health concerns. 3D Optoelectronic systems (BTS SmartDX) are standard devices for motion measurement, but their price, complexity, and size prevent them from being used outside of designated laboratories. This study was designed to evaluate the accuracy and reliability of an inertial measurement unit (IMU) in gauging cervical spine motion among healthy volunteers, using a 3D optoelectronic motion capture system as a reference. Twelve healthy volunteers participated in the study. They underwent lifting, transferring, and tilting simulations using a long spinal board, a Sked stretcher, and a vacuum mattress. During these simulations, cervical spine angular movements-including flexion-extension, axial rotation, and lateral flexion-were concurrently measured using the IMU and an optoelectronic device. We employed the Wilcoxon signed-rank test and the Bland-Altman plot to assess reliability and validity. A single statistically significant difference was observed between the two devices in the flexion-extension plane. The mean differences across all angular planes ranged from -1.129° to 1.053°, with the most pronounced difference noted in the lateral flexion plane. Ninety-five percent of the angular motion disparities ascertained by the SmartDX and IMU were less than 7.873° for the lateral flexion plane, 11.143° for the flexion-extension plane, and 25.382° for the axial rotation plane. The IMU device exhibited robust validity when assessing the angular motion of the cervical spine in the axial rotation plane and demonstrated commendable validity in both the lateral flexion and flexion-extension planes.

Digital-analog hybrid control of an inverted pendulum robot based on memristor

Abstract Single inertial measurement unit sensor signals are inaccurate, and traditional digital processing systems with separate storage and computation architectures cannot directly handle analog sensor signals, leading to significant delays and high power consumption. In that case, the control capability for self-balancing robots has been limited. In this work, a three-terminal MoS2-based memristor device was fabricated and successfully integrated into a memristor-based hybrid control inverted pendulum robot system. This system achieved continuous multi-sensor analog data fusion computations and analog PD control computations by combining the least squares method and a gradient descent algorithm based on a reward mechanism in digital circuits to control the memristors dynamically. Additionally, simulation testing and real, inverted pendulum robot control confirmed the efficacy of its data fusion and the accuracy of its PD control. The comparative experiments demonstrated that the hybrid control system based on memristors was approximately 86.41 mJ lower than the digital system. This system holds significant importance for the processing of large-scale sensor data and robot control.

Predicting 3D printed plastic part properties: A deep learning approach with thermographic and vibration data fusion

Additive Manufacturing (AM) holds transformative potential for the manufacturing industry, yet its widespread adoption is hindered by inconsistent product properties. This study addresses this challenge by pioneering a novel predictive method to assess the impact of in-process sensing on part property prediction in Fused Deposition Modeling (FDM), serving as a representative case study. Focused on five critical mechanical properties, including surface roughness, tensile strength, elongation at break, micro-hardness, and warpage, we systematically explore various sensor combinations by integrating Inertial Measurement Unit (IMU) sensors and a thermal camera with machine setting parameters. Utilizing deep learning models, specifically hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures, we encode time-series sensor data into signal images and evaluate performance across different sensor configurations. Our best-performing model achieves an impressive 99% correlation in predicting tensile strength, albeit with less robust performance in predicting warpage and micro-hardness, suggesting additional influencing factors. Furthermore, the employed signal imaging technique outperforms a handcrafted feature selection alternative. These findings carry significant implications for advancing the broader adoption of AM in critical applications, addressing challenges associated with inconsistent product properties and unlocking its full potential.

Enhancing Reliability of Inertial Measurement Unit Sensors in Quadrotor Drones

Enhancing Reliability of Inertial Measurement Unit Sensors in Quadrotor Drones

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Decision Tree-Based Direction Detection Using IMU Data in Autonomous Robots

Location detection plays a crucial role in various applications. In this study, a machine learning (ML) method using inertial measurement unit (IMU) data was developed to determine direction with the Global Positioning System (GPS). In this study, an electronic board was designed using an Arduino Mega, Altimu-10 IMU sensor, GPS module, and SD card module. This electronic board was placed on a car to create a new dataset. This dataset consists of 1952x11 data. The dataset was obtained using accelerometer (x, y, z), gyroscope (x, y, z), compass (x, y, z), and GPS sensor data. The Decision Tree Algorithm was proposed for direction determination in this study. The angles between each position and the previous position were calculated using the latitude and longitude values obtained from the collected data. Then, the data were divided into 4 classes: North, East, South, and West, based on specific angle ranges. Finally, a direction detection model was developed using IMU data in the proposed method, achieving an accuracy of approximately 82.11%.