Inertial Measurement Unit Sensor Research Articles

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks.

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Road slope estimation for heavy-duty vehicles under the influence of multiple source factors in real complex road environments

Accurate road slope information is of paramount importance for optimizing vehicle control and enhancing energy efficiency. In the context of heavy-duty vehicles (HDVs), the precise estimation of road slope presents a formidable challenge. This challenge is rooted in the coupling between vehicle mass and road slope, the robustness of road slope estimation methods across diverse driving styles, and the generalizability of algorithms to real-world road scenarios. To address these challenges, this paper proposes an innovative road slope estimation scheme that comprehensively considers the influences of multiple source factors in a real-world HDV transportation scenario. Firstly, in order to solve the problem of road slope estimation error under complex road types and different driving styles, a complex road slope estimation method based on the nonlinear predictive filter (NPF) combined with the Cubature Kalman filter (CKF) is constructed. To eliminate the problem of vehicle power fluctuation in special traffic scenarios, we propose a special scenario road slope estimation method based on the combination of a low-pass filter (LPF) and the NPF_CKF algorithm. To address the error problem when using low-cost Inertial Measurement Unit (IMU) sensors, we designed a Kalman filter (KF)-based multi-sensor fusion correction method. Finally, we conduct real-world experiments on complex roads at the test site to verify the accuracy of the proposed estimation method and its insensitivity to model parameters. The robustness and applicability of the proposed method are also verified in free-driving experiments conducted in real-world road scenarios.

NONAN GaitPrint: An IMU gait database of healthy young adults

An ongoing thrust of research focused on human gait pertains to identifying individuals based on gait patterns. However, no existing gait database supports modeling efforts to assess gait patterns unique to individuals. Hence, we introduce the Nonlinear Analysis Core (NONAN) GaitPrint database containing whole body kinematics and foot placement during self-paced overground walking on a 200-meter looping indoor track. Noraxon Ultium MotionTM inertial measurement unit (IMU) sensors sampled the motion of 35 healthy young adults (19–35 years old; 18 men and 17 women; mean ± 1 s.d. age: 24.6 ± 2.7 years; height: 1.73 ± 0.78 m; body mass: 72.44 ± 15.04 kg) over 18 4-min trials across two days. Continuous variables include acceleration, velocity, position, and the acceleration, velocity, position, orientation, and rotational velocity of each corresponding body segment, and the angle of each respective joint. The discrete variables include an exhaustive set of gait parameters derived from the spatiotemporal dynamics of foot placement. We technically validate our data using continuous relative phase, Lyapunov exponent, and Hurst exponent—nonlinear metrics quantifying different aspects of healthy human gait.

A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time.

IMU and Bluetooth Data Fusion to Achieve Submeter Position Accuracy in Indoor Positioning

Indoor navigation applications have become widespread in recent years with the ability of mobile phones which determine the position. Due to the inefficiency of global positioning system (GPS) indoors, other positioning methods have been developed based on local networks using technologies such as Bluetooth, wireless networks, ultra-wideband signals, ultrasonic signals, and radio frequency identification modules. Various technologies yield high or medium accuracy. Combining data from multiple sources via fusion enhances location precision. In this study, indoor positions were estimated using trilateration with Bluetooth devices, and the accuracy was improved by applying filters to the data from inertial measurement unit (IMU) sensors on the phone. As a result of combining Bluetooth and IMU data with data fusion, submeter accuracy was achieved. The results obtained were tested at Yildiz Technical University-Istanbul Türkiye. It was determined that 92% of the data was obtained with submeter accuracy.

Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

Effect of three different running gait cues on vertical tibial acceleration

BackgroundRepetitive impacts during running are associated with some running injuries. Tibial acceleration is a proxy for tibial loading, and interventions that can decrease it are likely to be of interest to the running community. Research questionWhat effect do running gait cues have on peak vertical tibial acceleration at a comfortable and moderate running pace, and how will these cues be executed? MethodsTwenty-seven participants ran on a treadmill according to the following cues in random order: run softly and lightly, run with shorter steps, and increase preferred step rate by 7.5 %. Participants maintained each condition for one minute before returning to their ‘preferred’ running pattern for two minutes. Two tibia-mounted inertial measurement unit sensors were used to collect tibial acceleration data at a ‘comfortable’ and ‘moderate’ run pace. A repeated measures analysis of variance test was used to compare the means between running conditions. ResultsCompared to preferred running, the decrease step length (−8 %, p = 0.002, Cohen’s d=0.33) and run softly and lightly (−9 %, p = 0.040, Cohen’s d=0.38) cues provided a significant reduction in peak vertical tibial acceleration during a comfortable run pace. No significant difference was observed with an increase in step rate. Compared to preferred running, there was no significant difference in peak vertical tibial acceleration with any of the running gait cues during a moderate run pace. SignificanceInstructing runners to decrease step length or run softly and lightly can result in small reductions in peak vertical tibial acceleration during a comfortable run pace, but running gait cues during a moderate run pace provide no effects. Differences in the execution of each cue are likely to influence overall tibial loading throughout the stride cycle. These findings provide novel biomechanical evidence for the potential effects of running retraining strategies for reducing tibial accelerations at different running paces.

EMG and IMU Data Fusion for Locomotion Mode Classification in Transtibial Amputees

Despite recent advancements in prosthetic technology, lower-limb amputees often remain limited to passive prostheses, which leads to an asymmetric gait and increased energy expenditure. Developing active prostheses with effective control systems is important to improve mobility for these individuals. This study presents a machine-learning-based approach to classify five distinct locomotion tasks: ground-level walking (GWL), ramp ascent (RPA), ramp descent (RPD), stairs ascent (SSA), and stairs descent (SSD). The dataset comprises fused electromyographic (EMG) and inertial measurement unit (IMU) signals from twenty non-amputated and five transtibial amputated participants. EMG sensors were strategically positioned on the thigh muscles, while IMU sensors were placed on various leg segments. The performance of two classification algorithms, support vector machine (SVM) and long short-term memory (LSTM), were evaluated on segmented data. The results indicate that SVM models outperform LSTM models in accuracy, precision, and F1 score in the individual evaluation of amputee and non-amputee datasets for 80–20 and 50–50 data distributions. In the 80–20 distribution, an accuracy of 95.46% and 95.35% was obtained with SVM for non-amputees and amputees, respectively. An accuracy of 93.33% and 93.30% was obtained for non-amputees and amputees by using LSTM, respectively. LSTM models show more robustness and inter-population generalizability than SVM models when applying domain-adaptation techniques. Furthermore, the average classification latency for SVM and LSTM models was 19.84 ms and 37.07 ms, respectively, within acceptable limits for real-time applications. This study contributes to the field by comprehensively comparing SVM and LSTM classifiers for locomotion tasks, laying the foundation for the future development of real-time control systems for active transtibial prostheses.

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.

Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

Training, children, and parents: Coach perspectives on wearable sensor data in sub-elite figure skating in the United States

Motion data from wearable inertial measurement unit (IMU) sensors is widely used in some sports but is not widely used in figure skating. In order to understand how coaches perceive the use of IMU data in figure skating training, we conducted semi-structured interviews with 8 figure skating coaches at the sub-elite level in the United States. Unlike other qualitative studies involving the role of data in sports training, our study involves amateur athletes who are children or adolescents and who work closely with a coach. Based on those interviews, we found that coaches see themselves as the gatekeeper for sharing and interpreting data, coaches consider the varying needs of individual athletes when determining how to share data with that athlete, and that coaches are cautious when sharing data with parents. Based on these themes, we propose a discussion around a whiteboard as a paradigm for presenting data and suggest abstraction of numerical data to support sharing data with skaters and parents.

Can inertial measurement unit sensors evaluate foot kinematics in drop foot patients using functional electrical stimulation?

The accuracy of inertial measurement units (IMUs) in measuring foot motion in the sagittal plane has been previously compared to motion capture systems for healthy and impaired participants. Studies analyzing the accuracy of IMUs in measuring foot motion in the frontal plane are lacking. Drop foot patients use functional electrical stimulation (FES) to improve walking and reduce the risk of tripping and falling by improving foot dorsiflexion and inversion-eversion. Therefore, this study aims to evaluate if IMUs can estimate foot angles in the frontal and sagittal planes to help understand the effects of FES on drop foot patients in clinical settings. Two Gait Up sensors were used to estimate foot dorsi-plantar flexion and inversion-eversion angles in 13 unimpaired participants and 9 participants affected by drop foot while walking 6 m in a straight line. Unimpaired participants were asked to walk normally at three self-selected speeds and to simulate drop foot. Impaired participants walked with and without FES assistance. Foot angles estimated by the IMUs were compared with those measured from a motion capture system using curve RMSE and Bland Altman limits of agreement. Between participant groups, overall errors of 7.95° ± 3.98°, -1.12° ± 4.20°, and 1.38° ± 5.05° were obtained for the dorsi-plantar flexion range of motion, dorsi-plantar flexion at heel strike, and inversion-eversion at heel strike, respectively. The between-system comparison of their ability to detect dorsi-plantar flexion and inversion-eversion differences associated with FES use on drop foot patients provided limits of agreement too large for IMUs to be able to accurately detect the changes in foot kinematics following FES intervention. To the best of the authors' knowledge, this is the first study to evaluate IMU accuracy in the estimation of foot inversion-eversion and analyze the potential of using IMUs in clinical settings to assess gait for drop foot patients and evaluate the effects of FES. From the results, it can be concluded that IMUs do not currently represent an alternative to motion capture to evaluate foot kinematics in drop foot patients using FES.

DESIGN OF WIRELESS CONTROL SYSTEMS AND NAVIGATION SYSTEMS ON THE AUTONOMOUS VEHICLES AT HEAVY EQUIPMENT COMPANY

In this paper, the research development of the Autonomous Vehicle Prototype project has been carried out. The research was carried out on a golf cart (buggy) by designing wireless control and navigation systems. Designing a wireless control system on a golf cart aims to control the drive systems, steer systems, and brake systems. Furthermore, this system can be controlled electrically wirelessly through a Joystick Radio Controller, so that the golf cart can move forward, reverse, turn right and turn left. The navigation system aims to enable golf carts to track from point A to point B by using several sensors, including the GPS (Global Positioning System) sensor. GPS is used for tracking position on the golf cart, IMU (Inertia Measurement Unit) as a measure of the golf cart heading degree, LIDAR (Light Distance and Ranging) is used to detect distance and avoid obstacles, and Hall Effect Sensor as a speed sensor. The integration of the sensor and actuator systems is carried out using an Arduino Mega 2560 type microcontroller. Next, the movement from point A to point B is carried out autonomously. The final result of this research is that the GPS sensor can detect a track distance of 68.8 m with a minimum accuracy of 3 m and a resolution of 1 m. The IMU sensor can detect headings of 90o with 2o accuracy and 1o resolution. The LIDAR sensor can detect obstacles at a distance of 0 m – 4 m, and the Hall Effect sensor can detect the RPM of the car at a speed of 5 km/h. Golf cart control can be done wirelessly and automatically on the navigation system from point A to point B by avoiding obstacles.

An Integrated Approach for Real-Time Monitoring of Knee Dynamics with IMUs and Multichannel EMG.

Measuring human joint dynamics is crucial for understanding how our bodies move and function, providing valuable insights into biomechanics and motor control. Cerebral palsy (CP) is a neurological disorder affecting motor control and posture, leading to diverse gait abnormalities, including altered knee angles. The accurate measurement and analysis of knee angles in individuals with CP are crucial for understanding their gait patterns, assessing treatment outcomes, and guiding interventions. This paper presents a novel multimodal approach that combines inertial measurement unit (IMU) sensors and electromyography (EMG) to measure knee angles in individuals with CP during gait and other daily activities. We discuss the performance of this integrated approach, highlighting the accuracy of IMU sensors in capturing knee joint movements when compared with an optical motion-tracking system and the complementary insights offered by EMG in assessing muscle activation patterns. Moreover, we delve into the technical aspects of the developed device. The presented results show that the angle measurement error falls within the reported values of the state-of-the-art IMU-based knee joint angle measurement devices while enabling a high-quality EMG recording over prolonged periods of time. While the device was designed and developed primarily for measuring knee activity in individuals with CP, its usability extends beyond this specific use-case scenario, making it suitable for applications that involve human joint evaluation.

Prediction of Spasticity through Upper Limb Active Range of Motion in Stroke Survivors: A Generalized Estimating Equation Model.

We aim to study the association between spasticity and active range of motion (ROM) during four repetitive functional tasks such as cone stacking (CS), fast flexion-extension (FFE), fast ball squeezing (FBS), and slow ball squeezing (SBS), and predicted spasticity models. An experimental study with control and stroke groups was conducted in a Medical Center. A total of sixty-four participants, including healthy control (n = 22; average age (years) = 54.68 ± 9.63; male/female = 12/10) and chronic stroke survivors (n = 42; average age = 56.83 ± 11.74; male/female = 32/10) were recruited. We employed a previously developed smart glove device mounted with multiple inertial measurement unit (IMU) sensors on the upper limbs of healthy and chronic stroke individuals. The recorded ROMs were used to predict subjective spasticity through generalized estimating equations (GEE) for the affected side. The models have significant (p ≤ 0.05 *) prediction of spasticity for the elbow, thumb, index, middle, ring, and little fingers. Overall, during SBS and FFE activities, the maximum number of upper limb joints attained the greater average ROMs. For large joints, the elbow during CS and the wrist during FFE have the highest average ROMs, but smaller joints and the wrist have covered the highest average ROMs during FFE, FBS, and SBS activities. Thus, it is concluded that CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for the thumb and finger joints in chronic stroke survivors.

A Novel Internet of Things-Based System for Ten-Pin Bowling

Bowling is a target sport that is popular among all age groups with professionals and amateur players. Delivering an accurate and consistent bowling throw into the lane requires the incorporation of motion techniques. Consequently, this research presents a novel IoT Cloud-based system for providing real-time monitoring and coaching services to bowling athletes. The system includes two inertial measurement units (IMUs) sensors for capturing motion data, a mobile application, and a Cloud server for processing the data. First, the quality of each phase of a throw is assessed using a Dynamic Time Warping (DTW)-based algorithm. Second, an on-device-level technique is proposed to identify common bowling errors. Finally, an SVM classification model is employed for assessing the skill level of bowler athletes. We recruited nine right-handed bowlers to perform 50 throws wearing the two sensors and using the proposed system. The results of our experiments suggest that the proposed system can effectively and efficiently assess the quality of the throw, detect common bowling errors, and classify the skill level of the bowler.

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.

Gait characteristics in patients with distal radius fracture using an in-shoe inertial measurement system at various gait speeds

BackgroundDistal radius fractures (DRF) commonly occur in early postmenopausal females as the first fragility fracture. Although the incidence of DRF in this set of patients may be related to a lower ability to control their balance and gait, the detailed gait characteristics of DRF patients have not been examined. Research questionIs it possible to identify the physical and gait features of DRF patients using in-shoe inertial measurement unit (IMU) sensors at various gait speeds and to develop a machine learning (ML) algorithm to estimate patients with DRF using gait? MethodsIn this cross-sectional case control study, we recruited 28 postmenopausal females with DRF as their first fragility fracture and 32 age-matched females without a history of fragility fractures. The participants underwent several physical and gait tests. In the gait performance test, the participants walked 16 m with the in-shoe IMU sensor at slower, preferred, and faster speeds. The gait parameters were calculated by the IMU, and we applied the ML technique using the extreme gradient boosting (XGBoost) algorithm to predict the presence of DRF. ResultsThe fracture group showed lower hand grip strength and lower ability to change gait speed. The difference in gait parameters was mainly observed at faster speeds. The amplitude of the change in the parameters was small in the fracture group. The XGBoost model demonstrated reasonable accuracy in predicting DRFs (area under the curve: 0.740), and the most relevant variable was the stance time at a faster speed. SignificanceGait analysis using in-shoe IMU sensors at different speeds is useful for evaluating the characteristics of DRFs. The obtained gait parameters allow the prediction of fractures using the XGBoost algorithm.

Sarcopenia classification model for musculoskeletal patients using smart insole and artificial intelligence gait analysis.

The relationship between physical function, musculoskeletal disorders and sarcopenia is intricate. Current physical function tests, such as the gait speed test and the chair stand test, have limitations in eliminating subjective influences. To overcome this, smart devices utilizing inertial measurement unit sensors and artificial intelligence (AI)-based methods are being developed. We employed cutting-edge technologies, including the smart insole device and pose estimation based on AI, along with three classification models: random forest (RF), support vector machine and artificial neural network, to classify control and sarcopenia groups. Patient data of 83 individuals were divided into train and test sets, with approximately 67% allocated for training. Classification models were implemented using RStudio, considering individual and combined variables obtained through pose estimation and smart insole measurements. Performance evaluation of the classification models utilized accuracy, precision, recall and F1-score indicators. Using only pose estimation variables, accuracy ranged from 0.92 to 0.96, with F1-scores of 0.94-0.97. Key variables identified by the RF model were 'Hip_dif', 'Ankle_dif' and 'Hipankle_dif'. Combining variables from both methods increased accuracy to 0.80-1.00, with F1-scores of 0.73-1.00. In our study, a classification model that integrates smart insole and pose estimation technology was assessed. The RF model showed impressive results, particularly in the case of the Hip and Ankle variables. The growth of advanced measurement technologies suggests a promising avenue for identifying and utilizing additional digital biomarkers in the management of various disorders. The convergence of AI technologies with diagnostics and treatment approaches a promising future for enhanced interventions in conditions like sarcopenia.

An IMU-based robotic manipulator for versatile applications

With the development of sensory technologies, the inertial measurement unit (IMU) has been emerging widely. In this paper, the IMU sensor is used to facilitate the versatile applications of robotic manipulators. The Kalman filter fuses the raw measurements of an IMU mounted on human hands to obtain the real-time attitude information. The contribution is to manipulate the robotic arm dexterously by the motion guidance of human hands, to achieve human-robot interaction. The experimental results evaluate the attitude preciseness and associated manipulators locomotion. In the future work, the proposed interaction method will be applied in manipulation tasks such as grasping, inspection and so on.