Single Inertial Measurement Unit Sensor Research Articles

Real-Time Forward Head Posture Detection and Correction System Utilizing an Inertial Measurement Unit Sensor

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications.

Seasonal Comparison of Pre-Adolescent Soccer Players' Physical Performance Using an Objective Physical Test Battery.

Soccer is a multifactorial sport, requiring physical, psychological, technical, and tactical skills to succeed. Monitoring and comparing physical characteristics over time is essential to assess players' development, customize training, and prevent injury. The use of wearable sensors is essential to provide accurate and objective physical data. In this longitudinal study, 128 male adolescent soccer players (from Under 12 to Under 19) were evaluated at two time points (pre- and post-season). Participants completed the Euleria Lab test battery, including stability, countermovement and consecutive jumps, agility, and quick feet tests. A single Inertial Measurement Unit sensor provided quantitative data on fifteen performance metrics. Percentage changes were compared to the Smallest Worthwhile Changes to assess significant changes over time. The results showed significant improvements in most variables, including a 19.7% increase in quick feet, 10.9% in stability, and 9.6% in countermovement jumps. In principal component analysis, we identified four principal components-strength-power, balance, speed-agility, and stiffness-that explained over 80% of the variance. These findings align with previous studies assessing seasonal changes in adolescent soccer players, showing that the proposed test battery seems to be adequate to highlight physical performance changes and provide coaches with meaningful data to customize training and reduce injury rates.

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.

Deep Learning Approach for Gait Detection for Precise Stimulation of FES to Correct Foot Drop

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient’s foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system.

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks.

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios).

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring.

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems’ performance, which can be useful to spread this approach in clinical settings.

Abstract TP61: Can A Single Motion Sensor Identify Lower Limb Movement Alterations Among Stroke Survivors?

Introduction: Hindrance in normal movement is common among stroke survivors (SS), leading to a larger prevalence of falling and serious injuries. Current methods for analyzing the movement restrictions (camera-based motion capture) are limited to laboratory/clinical environments. Meanwhile, the presence of Inertial Measurement Unit (IMU) sensors in our everyday lives (e.g., in smartphones) is ever increasing due to their portability, affordability, and accessibility. The purpose of this study was to determine whether a single IMU sensor can differentiate movements between SS and healthy control (HC). Methods: Turning (360°) was selected as the activity since it is a common and complex motion. Data was collected from 12 participants (6 SS, 6 HC) using an IMU sensor (Xsens®) placed on unaffected leg (shank) and was analyzed using a 3-step process: filtration (5 Hz cutoff frequency), segmentation (cycles – consecutive foot placement), and feature extraction. Analyzed features included duration of task (sec), number of cycles, mean angular velocity (deg/sec) of shank per cycle, ratio of stance-time to the duration of task (%), and range of flexion angle (deg) for shank. Results: Obtained values were larger in SS for number of cycles (SS: 5.8, HC: 3.2, p-value<0.01) and duration of task (7.8, 3.4, <0.01). Meanwhile, HC showed higher velocity of shank (unaffected) flexion (1.2, 1.7, <0.01) and the ratio of stance-time to the duration of task (57.7, 32.3, <0.01). Conclusion: A single IMU sensor successfully detected kinematic disparities between two groups. Meanwhile, further work is required to examine the robustness and detection improvements upon using other sensor locations. The use of a single IMU sensor, also present in most smartphones/smartwatches, could enable this accessible technology to be used as a remote assessment/monitoring tool, extending the reach of diagnostic tools and rehabilitation programs.

Assessment of postural sway with a pendant-mounted wearable sensor

BackgroundBody-worn inertial measurement unit (IMU) sensors have been widely used in postural stability and balance studies because of their low cost and convenience. In most of these studies, a single IMU sensor is attached to a waist belt near the body’s center of mass. Some populations such as pregnant women, however, may find a waist belt challenging in terms of fit and comfort. For this reason it may be useful to identify an alternative location for placement of an IMU and a more comfortable means for attaching the sensor to the body.Research questionDoes placing an IMU sensor in a pendant worn around the neck permit discrimination between conditions with varying postural stability? MethodsTwenty-six healthy participants performed three standing tasks (double-leg, tandem, and single-leg standing) under eyes-open and eyes-closed vision conditions to preliminarily assess the ability of the pendant sensor to discriminate between balance conditions. Discrimination based upon data from a belt-mounted IMU was assessed in the same trials. Differences in standard deviation of acceleration components, sway area, and jerkiness due to trial condition and sensor were evaluated using analysis of variance followed by post hoc comparisons. These data were also incorporated into receiver-operator characteristic (ROC) curve analysis to assess the effectiveness of each sensor at discriminating between conditions. ResultsStability was found to vary across conditions, but there was no interaction between stability and sensor location (all p ≥ 0.323). ROC curve analysis showed that sensors in both locations were good discriminators between conditions.SignificancePlacing an IMU in a pendant may be feasible for studying and monitoring postural instability. This approach may be especially valuable when considering populations for which wearing a belt is uncomfortable.

Real-Time Limb Motion Tracking with a Single IMU Sensor for Physical Therapy Exercises.

Limb exercises are common in physical therapy to improve range of motion (RoM), strength, and flexibility of the arm/leg. To improve therapy outcomes and reduce cost, motion tracking systems have been used to monitor the user's movements when performing the exercises and provide guidance. Traditional motion tracking systems are based on either cameras or inertial measurement unit (IMU) sensors. Camera-based systems face problems caused by occlusion and lighting. Traditional IMU-based systems require at least two IMU sensors to track the motion of the entire limb, which is not convenient for use. In this paper, we propose a novel limb motion tracking system that uses a single 9-axis IMU sensor that is worn on the distal end joint of the limb (i.e., wrist for the arm or ankle for the leg). Limb motion tracking using a single IMU sensor is a challenging problem because 1) the noisy IMU data will cause drift problem when estimating position from the acceleration data, 2) the single IMU sensor measures the motion of only one joint but the limb motion consists of motion from multiple joints. To solve these problems, we propose a recurrent neural network (RNN) model to estimate the 3D positions of the distal end joint as well as the other joints of the limb (e.g., elbow or knee) from the noisy IMU data in real time. Our proposed approach achieves high accuracy with a median error of 7.2/7.1 cm for the wrist/elbow joint in leave-one-subject-out cross validation when tracking the arm motion, outperforming the state-of-the-art approach by more than 10%. In addition, the proposed model is lightweight, enabling real-time applications on mobile devices.Clinical relevance- This work has great potential to improve limb exercises monitoring and RoM measurement in home-based physical therapy. It is also cost effective and can be made available widely for immediate application.

Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults.

The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants’ TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults’ fall risks remotely through the evaluation of the TUG score during their daily walking.

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

Accurate and Robust Walking Speed Estimation With Adaptive Regression Models for Wrist-Worn Devices

With the rise of interest in watch-type and wristband-type devices, wrist-worn devices have become a growing market in the field of wearable activity tracking devices. However, activity tracking with wrist-worn device is more challenging than the task with the other parts of the body, since upper limb movements are uncoupled from the action of walking and reveal irregular movements. This paper proposes a walking speed estimation method with data from a six-axis inertial measurement unit (IMU), which is commonly mounted in wrist-worn devices, and user’s height information. The proposed method provides accurate walking speed estimation results under different sensor-carrying modes and walking speeds. The estimation is based on sensor-carrying mode detection with the TreeBagger model, and on Gaussian process regression (GPR) models, which are adapted to seven predetermined sensor-carrying modes. The speed estimation is done by calculating a weighted sum of multiple GPR models with the probabilities from TreeBagger model. To evaluate the superiority of our method, we implement it on a hardware. An experimental evaluation is performed on 18 healthy subjects with a treadmill. The experimental results show that the proposed method outperforms existing studies and comparable commercial devices for all sensing conditions. The averaged error of the proposed method is about 5% for all sensing conditions, while others show error of more than 15% in different sensing conditions. The results show that proposed method provides better accuracy compared to existing studies in terms of estimating walking speed accurately under changing sensing conditions only with single IMU sensor.

Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field

Manual load carrying without sufficient rest may cause work-related musculoskeletal disorders (WMSDs) and needs to be monitored at construction sites. While previous studies have been able to predict load-carrying modes using multiple wearable inertial measurement unit (IMU) sensors, wearing multiple sensors obtrudes on workers during various construction tasks. In this context, by using a single IMU sensor, this research proposes an automatic detecting technique for excessive carrying-load (DeTECLoad) to predict load-carrying weights and postures simultaneously. DeTECLoad converts the IMU data into image data using a Gramian Angular Field, and then uses a hybrid Convolutional Neural Network-Long Short-Term Memory to classify load-carrying modes from the image data. DeTECLoad provides 92.46% and 96.33% accuracies for the load-carrying weight and posture classifications, respectively. By exploiting DeTECLoad, a construction worker's excessive load-carrying tasks could be managed in situ, helping to prevent construction site WMSDs.

A Comparative Severity Assessment of Impaired Balance due to Cerebellar Ataxia using Regression Models.

Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test. Inertial Measurement Unit (IMU) data were collected from a sensor attached to their chest of 53 subjects while they performed the test. The corresponding clinical scores were also tabulated. Using this data, 99 features were extracted to quantify acceleration, tremor and displacement of body sway. These features were filtered to identify the subset that better characterize the distinctive behavior of CA subjects. Elastic Net Regression model resulted a greater agreement (0.70 Pearson coefficient) with the clinical SARA scores. The overall results indicated that data from a single IMU sensor is sufficient to accurately assess balance in CA. The significance of this study is that evaluation of balance using Recurrence Quantification Analysis produces a comprehensive framework for the assessment of CA.

Use of an instrumented dual-task timed up and go test in children with traumatic brain injury

BackgroundWearable sensors have allowed researchers to instrument tests of gait-related mobility, including the widely used timed ‘up-and-go’ test (TUG). Currently, there is a lack of instrumented test data on whether children with moderate to severe traumatic brain injury (TBI) perform differently on the TUG compared to typically developed (TD) controls during a cognitive-motor task. Research QuestionThe aim was to explore the effects of a cognitive-motor task on TUG subcomponents among children with TBI compared to TD children. MethodsThis observational cross-sectional study included 12 children with moderate to severe TBI (6 males and 6 females, age 10.5 ± 1.5 years of age) and 10 age and sex-matched TD controls (5 males and 5 females, 10.4 ± 1.3 years of age). Each participant completed 6 trials of the TUG wearing a single inertial measurement unit sensor at a self-selected walking pace while listening to an array of 10 randomly presented single digits during each TUG trial. ResultsTotal time to complete the TUG was not significantly different between groups. The cognitive-motor task led to significantly lower mean turn and peak turn angular velocity values during the turn-around-the-cone and turn-before-sitting TUG subcomponents in children with TBI compared to the TD controls (p ≤ 0.05). Additionally, the cognitive-motor task led to significantly lower values for maximum torso flexion to extension angle, peak flexion and extension angular velocity and peak vertical acceleration for the sit-to-stand subcomponent (p < 0.05). Peak flexion angular velocity during the stand-to-sit subcomponent was lower for the TBI group compared to the children with TD (p < 0.05). SignificanceThe study provides new insights into the performance of complex gait-related mobility tasks in the context of an instrumented TUG among children with moderate to severe TBI. Our results highlight the potential benefits of outfitting pediatric inpatients with an IMU while completing the TUG.

허리 장착 기반의 IMU 센서를 이용한 3D 보행자 위치 추정 알고리즘 연구

This paper proposes a three-dimensional pedestrian position estimation algorithm using a waist-mounted Inertial Measurement Unit (IMU) sensor. A Pedestrian Dead Reckoning (PDR) system has been implemented using a single IMU sensor to locate a pedestrian who is carrying the PDR system on his waist in a building in which GPS signals are too weak to be utilized for localization. The position estimation system using IMU data suffers from degraded accuracy caused by the bias drift of gyroscope values. This approach adopts the quaternion to reduce the bias drift and obtain the precise Euler angles to localize the pedestrian in two-dimensional space with the step length estimation. To extend the localization into three-dimensional space to incorporate situations in which the pedestrian is ascending or descending the stairs, Zero-Velocity UpdaTe (ZUPT) scheme has been applied for processing gyroscope data from the IMU sensor. Real experiments have been performed to show the proposed algorithm’s effectiveness in both two- and three-dimensional spaces.

Validity of shoe-type inertial measurement units for Parkinson\u2019s disease patients during treadmill walking

BackgroundWhen examining participants with pathologies, a shoe-type inertial measurement unit (IMU) system with sensors mounted on both the left and right outsoles may be more useful for analysis and provide better stability for the sensor positions than previous methods using a single IMU sensor or attached to the lower back and a foot. However, there have been few validity analyses of shoe-type IMU systems versus reference systems for patients with Parkinson’s disease (PD) walking continuously with a steady-state gait in a single direction. Therefore, the purpose of this study is to assess the validity of the shoe-type IMU system versus a 3D motion capture system for patients with PD during 1 min of continuous walking on a treadmill.MethodsSeventeen participants with PD successfully walked on a treadmill for 1 min. The shoe-type IMU system and a motion capture system comprising nine infrared cameras were used to collect the treadmill walking data with participants moving at their own preferred speeds. All participants took anti-parkinsonian medication at least 3 h before the treadmill walk. An intraclass correlation coefficient analysis and the associated 95% confidence intervals were used to evaluate the validity of the resultant linear acceleration and spatiotemporal parameters for the IMU and motion capture systems.ResultsThe resultant linear accelerations, cadence, left step length, right step length, left step time, and right step time showed excellent agreement between the shoe-type IMU and motion capture systems.ConclusionsThe shoe-type IMU system provides reliable data and can be an alternative measurement tool for objective gait analysis of patients with PD in a clinical environment.