IMU Sensor Research Articles

RingVKB: A Ring-Shaped Virtual Keyboard Using Low-Cost IMU

Wearable devices have been important components for ubiquitous computing. However, text input remains challenging on wearables due to the lack of a physical keyboard. In this paper, we propose a novel ring-shaped virtual keyboard system named RingVKB for convenient text input using low-cost IMUs available on any wearables. At the core of RingVKB are two novel designs: 1) A circular keyboard layout with 12 equal sectors, which assembles all common keys on classical keyboards while allowing users to type with only one finger effectively, and 2) an error control algorithm that calculates the relative displacement of keystrokes from the noisy IMU sensor data. The two components, coupled together, enable high-accuracy and efficient text input for ubiquitous scenarios.We implement RingVKB using a small device consisting of a microcontroller and a MEMS sensor, which can be attached to the user's index finger.Experimental results show that RingVKB can effectively improve the relative displacement estimation accuracy, and achieves an overall keystroke recognition accuracy of 93% for 25 key positions. A user study also shows that RingVKB is easy to learn and use. Using only low-cost IMU sensors, RingVKB provides a virtual keyboard solution that can be widely adopted on wearables.

Real-Time Dense Construction With Deep Multiview Stereo Using Camera and IMU Sensors

Real-Time Dense Construction With Deep Multiview Stereo Using Camera and IMU Sensors

Characteristics of hoof landing in sound horses and the influence of trimming and shoeing examined with hoof-mounted inertial sensors.

The aim of the current in vivo, observational study was to investigate the effects of different hoof manipulations on landing duration (LandD), location (ICloc) and angle of initial contact (ICangle) in the front feet of horses. A novel, hoof-mounted, inertial measurement unit sensor system (IMU) was used. Ten sound, crossbred horses were fitted with an IMU sensor at the dorsal hoof wall and examined barefoot and after trimming. Additionally, the application of 120 g lateral weights and 5° medial side wedges as well as steel, aluminium, egg bar and lateral extension shoes were tested. Horses were guided in a straight line on firm ground. The use of steel shoes increased LandD compared to barefoot and enhanced the individual ICloc in trot. Application of rolled toe shoes caused a longer LandD than use of plain shoes. None of the other modifications significantly influenced the timing or spatial variables of hoof landing. Trimming and shoeing have less impact on the landing pattern of horses than assumed in practice. Still, the use of steel shoes changes sliding properties of the hooves on firm ground and increases weight causing a longer LandD and reinforcement of the individual ICloc.

Labview and Remotexy Integration for Quadrotor Stabilization and Control

Abstract Nowadays, Small quadcopters have made significant advancements in recent years, thanks to the development of control systems, the availability of sensors, and affordable and reliable materials for their production. Additionally, programs have been developed to model and analyze these aircraft before production. The professional applications of quadcopters are seemingly endless due to their many advantages. The aim of this research is to build a quadcopter and test its stability utilizing Arduino Mega, IMU sensor (Inertial Measurement Unit) and MPU-6050 in LabVIEW environment. The objective is to select the suitable PID parameters and create a remote-control program that can be operated using a smartphone and RemoteXY app on Android OS.

A Systematic Review of the Biomechanical Studies on Shoulder Kinematics in Overhead Sporting Motions: Types of Analysis and Approaches

The shoulder is a unique and complex joint in the human body with three bones and four joints, which makes it the most unstable joint in the body due to the amount of motion. To improve approaches toward understanding the performance of overhead throwing movements, this systematic review summarizes the type of analysis related to shoulder biomechanics involving overhead sporting motions. A search of seven databases identified 33 eligible studies, which were subsequently scored using the Modified Coleman Methodology Score scale. A total of nine articles from badminton, seven from baseball, five from volleyball, five from tennis, three from cricket, and one from softball were reviewed. All 33 studies evaluated shoulder kinematics and 12 of them also investigated the forces and torques (kinetics). The most common methods used were 3D motion analysis (76%), digital video cameras (15%), electromagnetic tracking system (6%), and finally 3% used IMU sensors. Overall, shoulder external rotation during the back swing, internal rotation, and elbow extension during the forward acceleration phase were the strongest predictors of high velocity overhead throwing movement. The findings provide some useful insights and guidance to researchers in their future contribution to the existing body of literature on shoulder overhead throwing movement biomechanics.

Telerehabilitation with ARC Intellicare to Cope with Motor and Respiratory Disabilities: Results about the Process, Usability, and Clinical Effect of the "Ricominciare" Pilot Study.

"Ricominciare" is a single-center, prospective, pre-/post-intervention pilot study aimed at verifying the feasibility and safety of the ARC Intellicare (ARC) system (an artificial intelligence-powered and inertial motion unit-based mobile platform) in the home rehabilitation of people with disabilities due to respiratory or neurological diseases. People with Parkinson's disease (pwPD) or post-COVID-19 condition (COV19) and an indication for exercise or home rehabilitation to optimize motor and respiratory function were enrolled. They underwent training for ARC usage and received an ARC unit to be used independently at home for 4 weeks, for 45 min 5 days/week sessions of respiratory and motor patient-tailored rehabilitation. ARC allows for exercise monitoring thanks to data from five IMU sensors, processed by an AI proprietary library to provide (i) patients with real-time feedback and (ii) therapists with information on patient adherence to the prescribed therapy. Usability (System Usability Scale, SUS), adherence, and adverse events were primary study outcomes. Modified Barthel Index (mBI), Barthel Dyspnea Index (BaDI), 2-Minute Walking Test (2MWT), Brief Fatigue Inventory (BFI), Beck Depression or Anxiety Inventory (BDI, BAI), and quality of life (EQ-5D) were also monitored pre- and post-treatment. A total of 21 out of 23 eligible patients were enrolled and completed the study: 11 COV19 and 10 pwPD. The mean total SUS score was 77/100. The median patients' adherence to exercise prescriptions was 80%. Clinical outcome measures (BaDI, 2MWT distance, BFI; BAI, BDI, and EQ-5D) improved significantly; no side effects were reported. ARC is usable and safe for home rehabilitation. Preliminary data suggest promising results on the effectiveness in subjects with post-COVID condition or Parkinson's disease.

Machine Learning for Human Motion Intention Detection.

The gait pattern of exoskeleton control conflicting with the human operator's (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot's intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors

This study proposes a telemanipulation framework with two wearable IMU sensors without human skeletal kinematics. First, the states (intensity and direction) of spatial hand-guiding gestures are separately estimated through the proposed state estimator, and the states are also combined with the gesture’s mode (linear, angular, and via) obtained with the bi-directional LSTM-based mode classifier. The spatial pose of the 6-DOF manipulator’s end-effector (EEF) can be controlled by combining the spatial linear and angular motions based on integrating the gesture’s mode and state. To validate the significance of the proposed method, the teleoperation of the EEF to the designated target poses was conducted in the motion-capture space. As a result, it was confirmed that the mode could be classified with 84.5% accuracy in real time, even during the operator’s dynamic movement; the direction could be estimated with an error of less than 1 degree; and the intensity could be successfully estimated with the gesture speed estimator and finely tuned with the scaling factor. Finally, it was confirmed that a subject could place the EEF within the average range of 83 mm and 2.56 degrees in the target pose with only less than ten consecutive hand-guiding gestures and visual inspection in the first trial.

Prevention and Rehabilitation Gaming Support for Ankle Injuries Usable by Semi-Professional Athletes Using Commercial Off-the-Shelf Sensors

Ankle injuries are amongst the most common musculoskeletal injuries. The necessity of prevention measurements before or an early rehabilitation start after an injury, is essential for (semi-) professional sports like soccer to decrease healing duration. Sensor-supported serious games could complement a therapeutic program to support resilience and motivation during the prevention or rehabilitation process. Therefore, the aim of this study is to develop and evaluate a user-centered prototype of a serious game using a commercial Off-The-Shelf MetaMotion IMU sensor. A semi-structured interview with a soccer club therapist, followed by an online questionnaire containing 48 questions (n = 91), was performed to ensure a user-centered approach. Based on this, a prototype, including five identified functional requirements and seven exercises (comprising: horizontal/vertical in- and eversion, dorsi- and plantarflexion, knee bend and squat, and toe and heel rise), was developed in an iterative process and evaluated by two participants with an acute ankle injury. The questionnaire outcomes showed averages of 3.3 ankle injuries per participant and 40 absence days per incident. Additionally, 85% of the participants reported needing more prevention time for such injuries. The evaluation phase (total training duration: 2 h 52 min) consisted of playing two different game types (1 and 2 degrees of freedom) and three different levels, where an avatar needs to be controlled while running and avoiding obstacles or collecting trophies. Both range of motion (ROM) and scores, which are directly measured by the game, showed significant improvements (ROM: t = 5.71; p < 0.01; Score: t = 3.98; p < 0.01) between the first and last session in both participants (P1: ROM +3.56°; Score +7.00%, P2: ROM +6.59°; Score +9.53%), indicating high effectiveness, despite a short training period (1 and 2 weeks). ROM improvement results and athlete feedback coincide in that the sensor-assisted serious game might be beneficial for ankle prevention and rehabilitation. At the same time, the increased scores indicate substantial motivation over several training sessions.

IMU Sensor Based Omnidirectional Robot Localization and Rotary Encoder

Localization is a technique to determine the position of the robot in an environment. Robot positioning is a basic problem when designing a mobile robot. If the robot does not know its position, then the next robot action will be difficult to determine. To be able to determine the position of the omnidirectional robot requires good speed control on the DC motor. In omnidirectional robots, positioning is through the use of a rotary encoder sensor to count the movement of the omni robot at X and Y coordinates and the IMU sensor to maintain the direction of the robot facing. PID control is also applied to control the rotational speed of each DC motor on the robot wheel. Odometry is the method used in this study. The odometry system aims to estimate the position relative to the initial position of the omni robot to estimate the change in position from time to time. The final result of this research is the application of the odometry method based on a rotary encoder and IMU sensor can produce an effective and stable robot motion and can move in all directions (holonomic) by maintaining the robot's facing direction. The results of the test form simple motions such as forward, backward, right side, and left side motion, as well as forming a box trajectory that has a position error that is not large and quite accurate. The average error value at coordinate X is 1.44 cm and at coordinate Y is 1.67 cm.

Performance Comparison of Different Fuzzy Logic Controllers on Vehicle-Caravan Systems

Active control techniques in Vehicle-Caravan (VC) systems are designed to prevent instability modes. This study used Fuzzy Logic Controllers (FLCs), developed using the differential braking method, to prevent instability modes in a VC system and increase yaw stability. Four different FLC-based controllers were designed for the VC system: type-1 Mamdani, type-1 Sugeno, simplified type-2, and Interval Type-2 (IT2). FLC-based controllers are used in VC systems due to nonlinear characteristics. This study showed that unstable situations can be prevented with FLCs according to the inputs obtained from a single IMU sensor placed in the caravan. The performance of the controllers developed in MATLAB/Simulink was assessed using CarSim. Experimental studies showed that the skidding that occurs after the Double Lane Change (DLC) maneuver is prevented by FLC-based controllers and the yaw stability is increased.

Enabling Guardian Angels: Designing and Constructing a Wireless Nurse CallSystem with IMU-Based Fall Detection for Enhanced Patient Safety

Falling poses a significant health concern across all age groups, with particular severityamong the elderly. Hospitalized patients, in particular, are vulnerable to injuries andevendeath due to falls. While patient supervision is essential for fall prevention, constant proximitybetween patients and healthcare staff is not always feasible. To tackle this challenge, thisstudy aimed to develop a solution that enables immediate assistance for patients who aredistant from the nurse call button when a fall occurs.The study employed the IMU sensor,which combines an accelerometer and a gyroscope. This sensor served as a transmitter todetect gravity acceleration and magnitude when afall event takes place. Thedata obtainedfrom the IMU sensor were further processed using an Arduino Uno microcontroller. Thesensor was integrated into a belt worn around the waist of the participants, who performedvarious movements such as falling facing down, falling up, falling to the right, falling to theleft, standing then sitting, and sitting then standing.The experimental tests yielded compellingresults, with all trials achieving an accuracy rate of 81.7%. The accuracy was determined byanalyzing the confusion matrix, which enabled accurate calculations.The utilization of thisinnovative tool significantly reduces the risk of patients experiencing detrimental outcomesfollowing falls by promptly notifying medical personnel, even when they aredistant from thenurse call button. Moreover, the implementation of this tool enhances overall safety forhospitalized patients, especially those at a high risk of falling. Future research can explore theintegration of additional sensors or the development of more sophisticated algorithms tofurther enhancethe accuracy and efficacy of this tool.

RANCANG BANGUN MODUL UKUR TEKANAN PIJAK TELAPAK KAKI ROBOT HUMANOID

Maintaining balance for humanoid robots is one of the core problems to be solved. Krakatau FC as a humanoid type robot performs balancing based on error information obtained from the IMU sensor. Information from the IMU alone is considered inadequate because it ignores the contours of the robot's stepping area which greatly influences the actions that must be taken to maintain balance. The focus of this research is to create a foot pressure measurement module that is compatible with the Dynamixel communication protocol. The sensor used to measure pressure is a Force Sensitive Resistor (FSR) sensor. The test results show the module's ability to measure the pressure exerted by the robot's legs and communicate with the robot's main controller using the Dynamixel protocol. From the test, it is also known that the modules made can help the robot's mechanical calibration process based on the comparison of the pressure between the right and left soles of the feet.

FiMec tremor stabilization spoon: design and active stabilization control of two DoF robotic eating devices for hand tremor patients.

This article is about vibration-damping robotic eating devices designed for use by people who have difficulty in eating due to hand tremors due to neuromuscular system disorder. The robotic eating device has two degrees of freedom (DoF). It contains an active controller structure to absorb vibrations in the y- and z-directions. In the handle part of the robotic eating device, there are two DC motors placed on the y- and z-axis, a three-axis IMU inertia sensor, an embedded system board, and a power unit. To absorb the vibration measured from the IMU sensor, the position control of the two motors to which the spoon is connected is provided by PID controllers. The part of the spoon (the pit surface) where the food is placed is tried to be kept constant. To test the vibration-damping performance of the control method, the dynamic model of the spoon along the eating kinematic trajectory was simulated in the SimMechanics environment using vibration data from ten tremor patients. The results show that the stabilization method can absorb the vibration in the hand of the person in the range of 84-99.409% and successfully provide the stabilization of the spoon tip. This damping rate is promising for providing a healthy diet for hand tremor patients.

Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

The extrinsic calibration of visual-inertial sensors has always been a complex problem for autonomous vehicles because the vehicle usually moves on a plane, rendering the calibration problem degenerate. Even if the vehicle drives on a slope, many great works are ineffective due to insufficient excitation. To address these challenges, we propose a method for calibrating the extrinsic parameters of camera and IMU sensors for vehicles, which involves two key contributions. The first is the introduction of a virtual vehicle reference frame, acting as a bridge to calibrate rotation in degenerate case. This involves formulating two least-squares problems to estimate the 3D relative rotation of the camera-IMU. Secondly, we propose an approach for estimating camera-IMU translation when a vehicle is navigating on a slope. This is done by solving a linear system, and these values are then refined using an algorithm that takes into account the gravitational magnitude. Extensive experimental results show that our proposed method effectively estimates rotation in planar motion and translation in sloping motion. Moreover, it achieves competitive accuracy and consistency when compared to state-of-the-art approaches.

Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice.

Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their own. Adherence to and quality of these home task practices are often limited, which is likely a factor reducing rehabilitation effectiveness post-stroke. However, home adherence is typically measured by self-reports that are known to be inconsistent with objective measurement. The objective of this study was to develop algorithms to enable the objective identification of task type and quality. Twenty neurotypical participants wore an IMU sensor on the wrist and performed four representative tasks in prescribed fashions that mimicked correct, compensatory, and incomplete movement qualities typically seen in stroke survivors. LSTM classifiers were trained to identify the task being performed and its movement quality. Our models achieved an accuracy of 90.8% for task identification and 84.9%, 81.1%, 58.4%, and 73.2% for movement quality classification for the four tasks for unseen participants. The results warrant further investigation to determine the classification performance for stroke survivors and if quantity and quality feedback from objective monitoring facilitates effective task practice at home, thereby improving motor recovery.

Learning Observation Model for Factor Graph Based-State Estimation Using Intrinsic Sensors

The navigation system of autonomous mobile robots has appeared challenging when using exteroceptive sensors such as cameras, LiDARs, and radars in textureless and structureless environments. This paper presents a robust state estimation system for holonomic mobile robots using intrinsic sensors based on adaptive factor graph optimization in the degradation scenarios. In particular, the neural networks are employed to learn the observation and noise model using only IMU sensor and wheel encoder data. Investigating the learning model for the holonomic mobile robot is discussed with various neural network architectures. We also explore the neural networks that are far more powerful and have cheaper computing power when using the inertial-wheel encoder sensors. Furthermore, we employ an industrial holonomic robot platform equipped with multiple LiDARs, cameras, IMU, and wheel encoders to conduct the experiments and create the ground truth without a bulky motion capture system. The collected datasets are then implemented to train the neural networks. Finally, the experimental evaluation presents that our solution provides better accuracy and real-time performance than other solutions. <i>Note to Practitioners</i>&#x2014;Autonomous mobile robots need to serve in challenging environments robustly that deny extrinsic sensors such as cameras, LiDARs, and radars. In order to operate in this situation, the navigation system shall rely on the intrinsic sensor as inertial sensor and wheel encoders. Existing conventional methods have combined the intrinsic sensors in the form of the recursive Bayesian filtering technique without adapting these models. Besides, deep learning-based solutions have adopted extensive networks like LSTM or CNN to handle the estimation problem. This work aims to develop a state estimation subsystem of the navigation system for the holonomic mobile robots that utilize the intrinsic sensors in adaptive factor graph optimization. In particular, we present how to join the factor graph efficiently with the learning observation model of IMU and wheel encoder factor. Moreover, the neural networks are introduced to learn the observation model with an IMU and wheel encoder data inputs. We recognize that lightweight neural networks can achieve more power than deep learning techniques using the IMU sensor and wheel encoders. Finally, the neural networks are embedded in a factor graph to handle the smoothing state estimation. The proposed system could operate with high accuracy in real time.

Numerical investigation on the effect of waist movement towards the golf putting outcome

Introduction: Golf is a sport that requires high accuracy and precision to obtain the best score. Throughout the game, accuracy and precision are most critical during putting. Putting is a golf stroke performed near or on the green area, which is supposed to be the final stroke to place the golf ball into the targeted hole. According to a study, putting can affect the golf score by up to 40%, highlighting its importance. Most of the time, golfers rely on qualitative feedback during putting practice that the golf ball ended up in or missed the hole. The qualitative feedback is subjective and unmeasurable. Aims: This study aims to provide golfers with numerical data to evaluate their putting performance. It focuses on the biomechanics feedback, specifically waist movement while putting. Methods: Three commercial IMU sensors by MbientLab were positioned along a golfer’s waist at three different locations. The golfer’s waist movement was recorded for several putting strokes. Results: Based on the findings, the waist movement certainly affects the putting outcome. It is found that minimum movement is preferable to increase the chances of having a straight putt. Conclusions: Golfers commonly rely on qualitative feedback to evaluate their putting; the golf ball ended up in or missed the hole. This kind of feedback is subjective, and golfers need information on how to move to obtain a good putting outcome. This study provides an insightful approach for golfers to analyse their putting biomechanics by providing numerical data for them to optimise their performance.

Integration of force and IMU sensors for developing low-cost portable gait measurement system in lower extremities

Gait analysis is the method to accumulate walking data. It is useful in diagnosing diseases, follow-up of symptoms, and rehabilitation post-treatment. Several techniques have been developed to assess human gait. In the laboratory, gait parameters are analyzed by using a camera capture and a force plate. However, there are several limitations, such as high operating costs, the need for a laboratory and a specialist to operate the system, and long preparation time. This paper presents the development of a low-cost portable gait measurement system by using the integration of flexible force sensors and IMU sensors in outdoor applications for early detection of abnormal gait in daily living. The developed device is designed to measure ground reaction force, acceleration, angular velocity, and joint angles of the lower extremities. The commercialized device, including the motion capture system (Motive-OptiTrack) and force platform (MatScan), is used as the reference system to validate the performance of the developed system. The results of the system show that it has high accuracy in measuring gait parameters such as ground reaction force and joint angles in lower limbs. The developed device has a strong correlation coefficient compared with the commercialized system. The percent error of the motion sensor is below 8%, and the force sensor is lower than 3%. The low-cost portable device with a user interface was successfully developed to measure gait parameters for non-laboratory applications to support healthcare applications.

Development of Construction Site Safety Monitoring Techniques Using Worker Positioning and BIM

Construction site accidents have been an ongoing concern, resulting in property damage and significant loss of life. Since the implementation of the Serious Accidents Punishment Act, the importance of preventing human casualties in industrial accidents has been increasingly emphasized, and safety management is being carried out through on-site supervision. However, safety management personnel are insufficient in expansive construction sites. In this study, we developed a technique that determines a worker’s position wearing a wearable sensor and manages the worker’s safety around a hazardous zone defined in the digital twin environment. By converging IMU and UWB sensors with the Kalman Filter, the worker’s positioning limit was improved, and BIM was utilized as a digital twin method. The findings demonstrate that it is possible to systematically manage worker-centered safety on construction sites by proposing preventive measures against the weakness of field supervisors as safety management personnel.