Examination of a practical method to quantitatively evaluate the rolling over movement in a clinical setting.

[Purpose] We investigated the utility of wearable inertial and magnetic sensing modules for analyzing neck and trunk movements during the rolling over movement. [Participants and Methods] The participants were instructed to roll over from the supine to the side-lying position with three sensor units attached to their forehead, xiphoid process of the sternum, and abdomen. Experiments were conducted on two prescribed patterns: one emphasizing hip joint flexion and adduction, and the other focusing on scapular protraction and horizontal shoulder joint adduction in two healthy participants (one male and one female). The flexion and rotation angles of the neck and trunk were calculated using conventional spreadsheet software with data obtained from the sensors. The obtained values were compared for agreement with those derived from a three-dimensional (3D) motion analysis device. [Results] The cross-correlation coefficient for the flexion and rotation angles of the neck and trunk between the two measurement methods was approximately 0.85, and the root mean square (RMS) angle difference was approximately 5.0°. [Conclusion] Wearable inertial and magnetic sensors can be used to quantitatively evaluate neck and trunk movements during the rolling over movement.

Inertial Sensor-Based Quantification of Movement Symmetry in Trotting Warmblood Show-Jumping Horses after "Limb-by-Limb" Re-Shoeing of Forelimbs with Rolled Rocker Shoes.

Hoof care providers are pivotal for implementing biomechanical optimizations of the musculoskeletal system in the horse. Regular visits allow for the collection of longitudinal, quantitative information ("normal ranges"). Changes in movement symmetry, e.g., after shoeing, are indicative of alterations in weight-bearing and push-off force production. Ten Warmblood show jumping horses (7-13 years; 7 geldings, 3 mares) underwent forelimb re-shoeing with rolled rocker shoes, one limb at a time ("limb-by-limb"). Movement symmetry was measured with inertial sensors attached to the head, withers, and pelvis during straight-line trot and lunging. Normalized differences pre/post re-shoeing were compared to published test-retest repeatability values. Mixed-model analysis with random factors horse and limb within horse and fixed factors surface and exercise direction evaluated movement symmetry changes (p < 0.05, Bonferroni correction). Withers movement indicated increased forelimb push-off with the re-shod limb on the inside of the circle and reduced weight-bearing with the re-shod limb and the ipsilateral hind limb on hard ground compared to soft ground. Movement symmetry measurements indicate that a rolled rocker shoe allows for increased push-off on soft ground in trot in a circle. Similar studies should study different types of shoes for improved practically relevant knowledge about shoeing mechanics, working towards evidence-based preventative shoeing.

Analytical quaternion-based bias estimation algorithm for fast and accurate stationary gyro-compassing.

This work introduces a novel approach to Strapdown Inertial Navigation System (SINS) alignment, distinct from recursive methods like Kalman filtering. The proposed methodology expedites bias error calculations by utilizing quaternion-based analytical relationships, which bypasses the slow convergence behavior associated with recursive algorithms, particularly in azimuth angle error estimation. In addition, the proposed approach demonstrates comparable accuracy to traditional fine alignment methods. Simulations and experiments validate that in contrast to the 10-min time requirement of traditional fine alignment methods (for azimuth angle estimation in stationary conditions), the proposed approach achieves the same accuracy within 20 s. However, limitations exist as the algorithm is applicable only in stationary conditions, and necessitating a high-grade IMU capable of measuring the earth's rotation rate.

DISPEL: A Python Framework for Developing Measures From Digital Health Technologies.

Goal: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials' data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.

Inertial Measurement Unit and Heart Rate Monitoring to Assess Cardiovascular Fitness of Manual Wheelchair Users during the Six-Minute Push Test.

Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs' physical health is the 6 min push test (6MPT), where the user propels themselves as far as they can in six minutes. However, reliance on observer input introduces subjectivity, while limited quantitative data inhibit comprehensive assessment. Incorporating sensors into the 6MPT can address these limitations. Here, ten MWUs performed the 6MPT with additional sensors: two inertial measurement units (IMUs)-one on the wheelchair and one on the wrist together with a heart rate wristwatch. The conventional measurements of distance and laps were recorded by the observer, and the IMU data were used to calculate laps, distance, speed, and cadence. The results demonstrated that the IMU can provide the metrics of the traditional 6MPT with strong significant correlations between calculated laps and observer lap counts (r = 0.947, p < 0.001) and distances (r = 0.970, p < 0.001). Moreover, heart rate during the final minute was significantly correlated with calculated distance (r = 0.762, p = 0.017). Enhanced 6MPT assessment can provide objective, quantitative, and comprehensive data for clinicians to effectively inform interventions in rehabilitation.

Vertical pelvic movement asymmetry and lameness location in ipsilateral combined forelimb and hindlimb lameness cases.

BACKGROUND: Compensatory vertical head and pelvis movement asymmetry may occur in trotting horses with a primary cause of lameness in one end of the body due to the weight shifting between limbs, leading to apparent combined forelimb and hindlimb lameness (CFHL). Little is known about CFHL patterns observed with body-mounted inertial sensors (BMIS) and regardless of their underlying mechanisms, compensatory and secondary lameness may complicate the definitive identification of the primary causes of lameness. OBJECTIVE: Determine associations between vertical pelvic movement asymmetry and location of primary lameness in ipsilateral CFHL cases where hindlimb lameness is solely impact or push-off type. STUDY DESIGN: Retrospective cohort. METHODS: From a body-mounted inertial sensor (BMIS) evaluated equine lameness database, we identified cases with a consistent, low-variability ipsilateral impact (IpI) or ipsilateral pushoff (IpP) hindlimb lameness in a straight-line trot and that had definitive diagnoses. Cases were categorised by lameness location to the limb(s), diagnosis, and ratio of the amplitude of forelimb to hindlimb lameness (Forea/Hinda). Differences in the numbers of IpI and IpP cases in these categories were analysed with chi-square tests, effect sizes, and odds ratios. RESULTS: Among the 2375 total lameness cases screened, 49 IpI and 36 IpP cases met the criteria for consistency, low variability, and definitive diagnosis. IpI cases were more likely than IpP cases to have forelimb-only lameness causes when Forea/Hinda >1 (OR = 43, 95% CI = 2.3-798). IpP cases were more likely than IpI cases to have hindlimb-only causes at both Forea/Hinda >1.0 (OR = 20, 95% CI = 2.2-200) and <1.0 (OR = 14, 95% CI = 2.9-66.7). Compared with IpI, IpP cases were more frequently diagnosed with tendon, suspensory ligament, or high-motion joint disorders in hindlimbs (OR = 3.6, 95% CI = 1.1-12.3) and less with unknown causes (OR = 13.2, 95% CI = 3.2-75.2). In IpI cases, positive forelimb regional anaesthesia often reduced hindlimb lameness, whereas in IpP cases, positive hindlimb regional anaesthesia typically lessened forelimb lameness. MAIN LIMITATIONS: Most cases were Quarter Horses. The likelihood of location and cause of lameness may be different for other breeds. CONCLUSIONS: The type of pelvic movement asymmetry observed in IpI and IpP cases is linked to the location and underlying cause of the primary lameness.

Evaluation of Patients' Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital.

This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient's level of walking independence.

Smartpaddle® as a New Monitoring Feature: A Comparison Between Inertial Measurement Unit- and Strain Gauge-Based Devices on Tethered Swimming Forces.

Smartpaddle® is a novel wearable device based on inertial measurement units (IMU) for in-field arm-stroke kinetics and kinematics analysis in swimming. However, the lack of data regarding its agreement and reliability, coupled with restricted access to raw data, emphasizes the need to evaluate it against a well-established strain gauge (SG) reference method for assessing swimming forces. Thus, this study aimed to investigate the agreement and reliability between the Smartpaddle® and strain gauge in a 30-s all-out arms-only tethered swimming test. Twelve trained young adult swimmers performed a test-retest 30-s all-out arms-only tethered swimming trial. Peak and mean forces were obtained from IMU (PFIMU and MFIMU) and SG (PFSG and MFSG) simultaneously. Statistical differences and correlations were found in both peak (PFSG = 158.46 ± 48.85 N, PFIMU = 75.47 ± 12.05 N, p < 0.001, r = 0.88) and mean (MFSG = 69.62 ± 16.36 N, MFIMU = 30.06 ± 5.42 N, p < 0.001, r = 0.84) forces between devices, presenting elevated systematic errors for both variables. No differences were found in IMU data between test-retest conditions in both peak (PFIMU = 75.47 ± 12.05 N, PFIMU = 75.45 ± 11.54 N, p = 0.99, ICC = 0.96) and mean (MFIMU = 30.06 ± 5.42 N, MFIMU = 30.21 ± 5.83 N, p = 0.80, ICC = 0.95) forces, with negligible systematic errors. In conclusion, although the Smartpaddle® device is not directly comparable to the strain gauge reference method, it has demonstrated high reliability levels in test-retest trials.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Feasibility Study of a Prototype Wearable Inertial Measurement Unit for Elderly Postural Sway Assessment.

Background: Falls are a major public health problem among older adults since they are a primary cause of injuries, functional decline and mortality. Identifying individuals susceptible to falls enables early intervention and prevention strategies. Currently, wearable sensors have emerged as a promising tool for assessing balance and mobility due to their affordability, compact size, and established efficacy. Therefore, the objective of the present study was to evaluate inertial measurement unit (IMU)-based postural sway metrics during quiet stance with four different bases of support and compare them among elderly individuals who are at risk of falling and those who are not. Methods: A triaxial IMU prototype was developed for evaluating postural sway during quiet stance, with various bases of support. Totally, 103 elderly participants with mean age of 68.5 ± 5.7 years were included. Sway metrics, including the root mean square (RMS) of magnitude, summation of range of signal (Range), summation of sway area (SA) and summation of distance (SD) were employed to detect sway perturbations. Results: All of the sway metrics revealed a significantly increasing magnitude of signal trajectory with a decreasing base of support. When comparing IMU sway metrics between groups of individuals at potential risk and non-risk of falls, statistically significant differences were observed in some variables, including RMS, Range, and SA during semi-tandem stance, and Range and SA during one-leg standing. Conclusions: The findings support earlier studies that demonstrated the objective nature of the IMU in assessing balance and predicting future risk of falls. Limited significant findings in this study may be due to the lower sampling rate of the IMU prototype (50 Hz) compared to commonly reported frequencies (100 Hz), as well as the inclusion of elderly ambulatory participants who were capable of being independent in their daily activities. The IMU is capable of providing comprehensive data, and detecting subtle changes, early signs of balance impairment and fall tendencies.

Decreased wrist rotation imitation abilities in children with autism spectrum disorder.

Introduction: While meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed. Methods: The current study proposed a wrist rotation imitation (WRI) task (a meaningless GMI assignment), and established a WRI ability evaluation system using low-cost wearable inertial sensors, which acquired the simultaneous data of acceleration and angular acceleration during the WRI task. Three metrics (i.e., total rotation time, rotation amplitude, and symmetry) were extracted from those data of acceleration and angular acceleration, and then were adopted to construct classifiers based on five machine learning (ML) algorithms, including k-nearest neighbors, linear discriminant analysis, naive Bayes, support vector machines, and random forests. To illustrate our technique, this study recruited 49 ASD children (aged 3.5-6.5 years) and 59 age-matched typically developing (TD) children. Results: Findings showed that compared with TD children, those with ASD may exhibit shorter total rotation time, lower rotation amplitude, and weaker symmetry. This implies that children with ASD might exhibit decreased WRI abilities. The classifier with the naive Bayes algorithm outperformed than other four algorithms, and achieved a maximal classification accuracy of 88% and a maximal AUC value of 0.91. Two metrics (i.e., rotation amplitude and symmetry) had high correlations with the gross and fine motor skills [evaluated by Gesell Developmental Schedules-Third Edition and Psychoeducational Profile-3 (PEP-3)]. While, the three metrics had no significant correlation with the visual-motor imitation abilities (evaluated by the subdomain of PEP-3) and the ASD symptom severity [evaluated by the Childhood Autism Rating Scale (CARS)] . Discussion: The strengths of this study are associated with the low-cost measurement system, correlation between the WRI metrics and clinical measures, decreased WRI abilities in ASD, and high classification accuracy.

Evaluation of deep oscillation therapy for the treatment of lumbar pain syndrome using motion capture systems: A systematic review.

Low back pain is a painful disorder that prevents normal mobilization, increases muscle tension and whose first-line treatment is usually non-steroidal anti-inflammatory drugs, together with non-invasive manual therapies, such as deep oscillation therapy. This systematic review aims to investigate and examine the scientific evidence of the effectiveness of deep oscillation therapy in reducing pain and clinical symptomatology in patients with low back pain, through the use of motion capture technology. To carry out this systematic review, the guidelines of the PRISMA guide were followed. A literature search was performed from 2013 to March 2022 in the PubMed, Elsevier, Science Director, Cochrane Library, and Springer Link databases to collect information on low back pain, deep oscillation, and motion capture. The risk of bias of the articles was assessed using the Cochrane risk of bias tool. Finally, they were included 16 articles and 5 clinical trials which met the eligibility criteria. These articles discussed the effectiveness of deep oscillation therapy in reducing pain, eliminating inflammation, and increasing lumbar range of motion, as well as analyzing the use of motion capture systems in the analysis, diagnosis, and evaluation of a patient with low back pain before, during and after medical treatment. There is no strong scientific evidence that demonstrates the high effectiveness of deep oscillation therapy in patients with low back pain, using motion capture systems. This review outlines the background for future research directed at the use of deep oscillation therapy as a treatment for other types of musculoskeletal injuries.

Top-Down Design Method of a Time Domain Accelerometer with Adjustable Resolution.

A top-down design methodology and implementation of a time domain sensor is presented in this paper. The acceleration resolution of the time domain sensor is equal to the time-measurement accuracy divided by the sensor sensitivity. Combined with the sensitivity formula, the acceleration resolution is proportional to the vibration amplitude, the time-measurement accuracy, and the third power of the resonant frequency. According to the available time-measurement accuracy and the desired acceleration resolution, the parameters including the vibration amplitude and the resonant frequency were theoretically calculated. The geometrical configuration of the time domain sensor device was designed based on the calculated parameters. Then, the designed device was fabricated based on a standard silicon-on-insulator process and a matched interface circuit was developed for the fabricated device. Experimental results demonstrated that the design methodology is effective and feasible. Moreover, the implemented sensor works well. In addition, the acceleration resolution can be tuned by adjusting the time-measurement accuracy and the vibration amplitude. All the reported results of this work can be expanded to other time domain inertial sensors, e.g., a gyroscope or tilt sensor.

Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks.

Wearable inertial measurement units (IMUs) are used increasingly to estimate biomechanical exposures in lifting-lowering tasks. The objective of the study was to develop and evaluate predictive models for estimating relative hand loads and two other critical biomechanical exposures to gain a comprehensive understanding of work-related musculoskeletal disorders in lifting. We collected 12,480 lifting-lowering phases from 26 subjects (15 men and 11 women) performing manual lifting-lowering tasks with hand loads (0-22.7 kg) at varied workstation heights and handling modes. We implemented a Hierarchical model, that sequentially classified risk factors, including workstation height, handling mode, and relative hand load. Our algorithm detected lifting-lowering phases (>97.8%) with mean onset errors of 0.12 and 0.2 seconds for lifting and lowering phases. It estimated workstation height (>98.5%), handling mode (>87.1%), and relative hand load (mean absolute errors of 5.6-5.8%) across conditions, highlighting the benefits of data-driven models in deriving lifting-lowering occurrences, timing, and critical risk factors from continuous IMU-based kinematics.

The study developed and validated algorithms for detecting and predicting exposure to various risk factors during diverse lifting-lowering tasks. These factors encompass the occurrence, timing, workstation height, handling mode, and relative hand position. This approach facilitates the extraction of contextual information related to lifting tasks conducted in real-world settings through a continuous stream of inertial sensor measurements. Consequently, it can enable automated risk assessment for lifting activities in the field.

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study.

BACKGROUND: Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis. METHODS: This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants' kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied. RESULTS: Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively). CONCLUSION: This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

Reliability of Xsens inertial measurement unit in measuring trunk accelerations: a sex-based differences study during incremental treadmill running.

Introduction: Inertial measurement units (IMUs) are utilized to measure trunk acceleration variables related to both running performances and rehabilitation purposes. This study examined both the reliability and sex-based differences of these variables during an incremental treadmill running test. Methods: Eighteen endurance runners performed a test-retest on different days, and 30 runners (15 females) were recruited to analyze sex-based differences. Mediolateral (ML) and vertical (VT) trunk displacement and root mean square (RMS) accelerations were analyzed at 9, 15, and 21 km·h-1. Results: No significant differences were found between test-retests [effect size (ES)<0.50)]. Higher intraclass correlation coefficients (ICCs) were found in the trunk displacement (0.85-0.96) compared to the RMS-based variables (0.71-0.94). Male runners showed greater VT displacement (ES = 0.90-1.0), while female runners displayed greater ML displacement, RMS ML and anteroposterior (AP), and resultant euclidean scalar (RES) (ES = 0.83-1.9). Discussion: The IMU was found reliable for the analysis of the studied trunk acceleration-based variables. This is the first study that reports different results concerning acceleration (RMS) and trunk displacement variables for a same axis in the analysis of sex-based differences.

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System.

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

Characterization of Sensitivity of Time Domain MEMS Accelerometer.

This paper characterizes the sensitivity of a time domain MEMS accelerometer. The sensitivity is defined by the increment in the measured time interval per gravitational acceleration. Two sensitivities exist, and they can be enhanced by decreasing the amplitude and frequency. The sensitivity with minor nonlinearity is chosen to evaluate the time domain sensor. The experimental results of the developed accelerometer demonstrate that the sensitivities span from -68.91 µs/g to -124.96 µs/g and the 1σ noises span from 8.59 mg to 6.2 mg (amplitude of 626 nm: -68.91 µs/g and 10.21 mg; amplitude of 455 nm: -94.51 µs/g and 7.76 mg; amplitude of 342 nm: -124.96 µs/g and 6.23 mg), which indicates the bigger the amplitude, the smaller the sensitivity and the bigger the 1σ noise. The adjustable sensitivity provides a theoretical foundation for range self-adaption, and all the results can be extended to other time domain inertial sensors, e.g., a gyroscope or an inclinometer.

High-Precision Inertial Sensor Charge Ground Measurement Method Based on Phase-Sensitive Demodulation.

Inertial sensors are the key payloads in space gravitational wave detection missions, and they need to ensure that the test mass (TM), which serves as the inertial reference, freely floats in the spacecraft without contact, so that the TM is not disturbed by the satellite platform and the cosmic environment. Space gravitational wave detection missions require that the residual acceleration of the TM should be less than 3×10-15ms-2Hz-1/2. However, the TM with charges will interact with surrounding conductors and magnetic fields, introducing acceleration noise such as electrostatic force and Lorentz force. Therefore, it is necessary to carry out charge management on the TM, in which the high-precision measurement of charge is crucial. Space gravitational wave detection missions require a residual charge measurement accuracy of 3×10-13C for the TM. In this paper, we design a high-precision inertial sensor charge measurement method based on phase-sensitive demodulation (PSD). By establishing a torsion pendulum rotation model based on the force modulation method, the characteristics of the TM torsion angle signal are analyzed. The PSD is used to extract the amplitude of the specific frequency signal component containing the charge information, and then to calculate the value of the accumulated charges. The method is compared with the Butterworth band-pass filtering method, and the simulation results show that the method has a higher measurement accuracy, shorter settling time, and stronger anti-interference ability, meeting the TM residual charge measurement accuracy index requirement.

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.