Measurement Unit Research Articles

Research on the yingzao chi restoration of palace buildings sans dougong in Ming and Qing Dynasties: a case study of Chongqing

As a unit of measurement for Chinese ancient construction, the yingzao chi (yingzaochi 营造尺) is of great significance to study long-term transformation of culture, construction skills, and the scale design rule of Chinese regional architectural heritage. But few scholars study the restoration method of the yingzao chi of palace buildings sans dougong (斗拱) in Chinese Ming and Qing Dynasties. Based on the yingzao chi theory, this paper studies and improves a theoretical method to help restore the yingzao chi of palace buildings sans dougong in Ming and Qing Dynasties. This method is not limited by the lack of reference of vernacular chi (xiangchi 乡尺) and can restore the yingzao chi of a single building through surveying data, and there is a smaller error in the calculation process. Three typical palace buildings without dougong in Ming and Qing Dynasties in Chongqing are selected as examples. The results show that the restored yingzao chi of these three buildings is highly similar, most likely 320.7–323.6 mm, which can verify the reliability of this method. This study can help to improve the research of yingzao chi and provide theoretical support for the protection of this kind of architectural heritage. Besides, the restoration of the yingzao chi provides a potential opportunity to explore how the technological and cultural of palace architecture spread, develop and blend.

Real-world gait and turning in individuals scheduled for total knee arthroplasty

BackgroundImproving mobility – specifically walking – is an important treatment goal of total knee arthroplasty. Objective indicators for mobility, however, are lacking in clinical evaluations. This study aimed to compare real-world gait and turning between individuals scheduled for total knee arthroplasty and healthy controls, using continuous monitoring with inertial measurement units. MethodsReal-world gait and turning data were collected for 5–7 days in individuals scheduled for total knee arthroplasty (n = 34) and healthy controls (n = 32) using inertial measurement units on the feet and lower back. Gait and turning parameters were compared between groups using a linear regression model. Data was further analyzed by stratification of gait bouts based on bout length, and turns based on turning angle and turning direction. FindingsDominant real-world gait speed was 0.21 m/s lower in individuals scheduled for total knee arthroplasty compared to healthy controls. Stride time was 0.05 s higher in individuals scheduled for total knee arthroplasty. Step time asymmetry was not different between the groups. Regarding walking activity, individuals scheduled for total knee arthroplasty walked 72 strides/h less than healthy controls, and maximum bout length was 316 strides shorter. Irrespective of the size of the turn, turning velocity was lower in individuals scheduled for total knee arthroplasty. InterpretationIndividuals scheduled for total knee arthroplasty showed specific walking and turning limitations in the real-world. Parameters derived from inertial measurement units reflected a rich profile of real-world mobility measures indicative of walking limitation of individuals scheduled for total knee arthroplasty, which may provide a relevant outcome dimension for future studies.

GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients' diet/exercise in the future.

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

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

IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition

One of the primary challenges in the field of human activity recognition (HAR) is the lack of large labeled datasets. This hinders the development of robust and generalizable models. Recently, cross modality transfer approaches have been explored that can alleviate the problem of data scarcity. These approaches convert existing datasets from a source modality, such as video, to a target modality, such as inertial measurement units (IMUs). With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well - as shown in proof of concepts such as IMUGPT. In this work, we conduct a large-scale evaluation of language-based cross modality transfer to determine their effectiveness for HAR. Based on this study, we introduce two new extensions for IMUGPT that enhance its use for practical HAR application scenarios: a motion filter capable of filtering out irrelevant motion sequences to ensure the relevance of the generated virtual IMU data, and a set of metrics that measure the diversity of the generated data facilitating the determination of when to stop generating virtual IMU data for both effective and efficient processing. We demonstrate that our diversity metrics can reduce the effort needed for the generation of virtual IMU data by at least 50%, which opens up IMUGPT for practical use cases beyond a mere proof of concept.

Electric Drive Units: A Set-Up for Investigating Function, Efficiency, and Dynamics

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission’s efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds.

Load increases IMU signal attenuation per step but reduces IMU signal attenuation per kilometre

BackgroundDespite deleterious biomechanics associated with injury, particularly as it pertains to load carriage, there is limited research on the association between physical demands and variables captured with wearable sensors. While inertial measurement units (IMUs) can be used as surrogate measures of ground reaction force (GRF) variables, it is unclear if these data are sensitive to military-specific task demands. Research questionCan wearable sensors characterise physical load and demands placed on individuals in different load, speed and grade conditions? MethodsData were collected on 20 individuals who were self-reportedly free from current injury, recreationally active, and capable of donning 23 kg in the form of a weighted vest. Each participant walked and ran on flat, uphill (+6 %) and downhill (-6 %) without and with load (23 kg). Data were collected synchronously from optical motion capture (OMC) and IMUs placed on the distal limb and the pelvis. Data from an 8-second window was used to generate a participant-based mean of OMC and IMU variables of interest. Repeated Measures ANOVA was used to measure main and interaction effects of load, speed, and grade. Simple linear regression was used to elucidate a relationship between OMC measures and estimated metabolic cost (EMC) to IMU measures. ResultsLoad reduces foot and pelvic accelerations (p<0.001) but elevate signal attenuation per step (p=0.044). Conversely, attenuation per kilometre is lowered with the addition of load (p=0.017). Uphill had the lowest attenuation per step (p=0.003) and kilometre (p≤0.033) in walking, while downhill had the greatest attenuation per step (p≤0.002) and per kilometre (p≤0.004). Attenuation measures are inconsistently moderately related to limb negative work (R≤0.57). EMC is moderately positively related to unloaded running (R≥0.39), and moderately negatively related to walking with and without load (R≤-0.52). SignificanceWhile load reduces peak accelerations at both the pelvis and foot. However, it may increase demand on the lower extremity to attenuate the signal between the two sensors with each step, while attenuation over time reduces with load.

Validity and Reliability of a Commercially Available Inertial Sensor for Measuring Barbell Mechanics during Weightlifting

This study aimed to assess the validity and reliability of a commercially available inertial measurement unit (Enode) for measuring barbell kinematics and kinetics during a snatch. In order to assess validity and within- and between-session reliability, thirteen competitive weightlifters conducted two snatches on two separate occasions at 85% of their one-repetition maximum. The Enode sensor was attached to the barbell, with each lift recorded via the device’s native application concurrently with an 11-camera motion capture system. Passing–Bablok regression indicated fixed and proportional bias in some horizontal measures of barbell mechanics but showed no bias in all but one vertical variable. Collectively, this suggests that the Enode is a valid tool in the measurement of vertically derived, but not horizontally derived, variables from barbell kinematics. Within- and between-session reliability showed moderate to excellent ICCs, with trivial to small differences between repetitions and between sessions. However, between-session reliability showed lower levels of variability and, thus, may help coaches identify changes in technique over time (between sessions) with good accuracy. Overall the Enode offers a practical and affordable option for coaches seeking to monitor weightlifting technique in training environments.

A multi-sensor fusion SLAM algorithm for indoor aerial robots

Nowdays, existing Light Detection and Ranging (LiDAR) based unknown-space-exploration Simultaneous Localization and Mapping (SLAM) methods have the problems of unstable mapping in feature-degraded environments and poor estimation accuracy in the vertical direction. To solve these problems, this study presents a multi-sensor fusion SLAM algorithm for indoor aerial robots as well as Unmanned Aerial Vehicles (UAVs). The sensors used in the multi-sensor fusion algorithm include optical flow sensor, barometer, Inertial Measurement Unit (IMU), and LiDAR. The proposed algorithm is named LiDAR-IMU-optical flow odometry fusion algorithm (Lioo). We derived and established a tightly coupled multi-sensor fusion framework based on factor graph optimisation. The IMU-optical flow pre-integration factor is proposed, and the barometer factor to the algorithm is added innovatively. Engineering applications are also realised in this paper. Flight experiments demonstrated that the proposed algorithm achieves more precise mapping and localisation of UAVs in indoor environments.

LDVI-SLAM: A Lightweight Monocular Visual-Inertial SLAM System for Dynamic Environments Based on Motion Constraints

Abstract Traditional Simultaneous Localization and Mapping (SLAM) systems are typically based on the assumption of a static environment. However, in practical applications, the presence of moving objects significantly reduces localization accuracy, limiting the system's versatility. To address the challenges of SLAM systems in dynamic environments, the academic community often employs computationally intensive methods such as deep learning, and some algorithms rely on expensive sensors (e.g., LiDAR or RGBD cameras) to obtain depth information. These factors increase computational complexity or hardware costs, complicating practical deployment. To improve localization accuracy and adaptability of SLAM systems in dynamic scenarios while maintaining low deployment costs, this paper proposes a dynamic environment robust monocular inertial SLAM system named LDVI-SLAM. The system uses more cost-effective sensors—monocular cameras and Inertial Measurement Unit (IMU)—along with lightweight computational methods. In LDVI-SLAM, first, the reliability of IMU data is verified. Then, using the ego-motion information provided by the IMU, along with epipolar constraint and an improved Rotation-aware Flow Vector Bound (R-FVB) constraint, dynamic feature points are eliminated. Additionally, this paper proposes a continuous tracking across interval frames method to enhance the distinction between static and dynamic feature points. Experimental results demonstrate that LDVI-SLAM performs effectively in dynamic environments and is easy to deploy. On the VIODE dataset, experimental results show that compared to the deep learning-based DynaSLAM, this method reduces the RMSE (Root Mean Square Error) of absolute trajectory error by 10.3%. Moreover, in terms of speed, under the same computing power, the single-frame processing speed of this method is comparable to ORB-SLAM3 and is two orders of magnitude faster than DynaSLAM, significantly outperforming deep learning-based SLAM algorithms. Experiments on the OMD dataset further prove that this method effectively avoids the risk of semantic classification errors, demonstrating better robustness and generality.

An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Design and Characterization of a Wearable Inertial Measurement Unit.

The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring.

Measuring the Effect of an Ergonomic Lecture on the Rapid Upper Limb Assessment Scores of Dental Assistant Students Using Inertial Sensor-Based Motion Capture-A Randomized Controlled Study.

Individuals working in the field of dentistry have a high prevalence of musculoskeletal disorders (MSDs) owing to monotonous and one-sided physical exertion. Inertial measurement units (IMU) are increasingly shifting into focus for assessing postural risk at work. Therefore, the present study aimed to evaluate the effects of an ergonomic lecture and training intervention on postural risk and MSDs in dental assistant students using inertial sensor-based motion capture (MoCap). Eighteen female dental assistant students (age: 19.44 ± 6.83 years; height: 164.59 ± 5.32 cm; weight: 64.88 ± 16.52 kg; BMI: 19.70 ± 4.89 kg/m2), randomly divided into intervention (n = 9) and control (n = 9) groups, participated in the present study. The participants completed the Nordic Questionnaire on MSD prevalence, after which a 90 s MoCap with Xsens IMU was performed. A lecture on ergonomics was provided, followed by a five-week intervention for the intervention group. Follow-up assessments were performed, and 5- and 18-week follow-up MSD questionnaires were administered. Mixed analysis of variance (MANOVA) showed a significant difference in the Rapid Upper Limb Assessment (RULA) and part-scores of the upper arm and wrist. Despite a reduction in MSDs, no significant differences in the time of measurement and groups were detected after the five-week training intervention and the 18-week follow-up questionnaire. A targeted ergonomics lecture was effective for dental assistant students, and technologies such as IMU improved workplace ergonomics in dentists. Further studies with a longer measurement periods, follow-up, and larger sample sizes are recommended.

Transliteration in the Translation of The Classic of Tea: Problems and Solutions

The Classic of Tea, authored by the Tea Sage Lu Yu, encompasses over 400 entries related to tea names, names of tea utensils, personal names, place names, dynastic titles, titles of literary works, Chinese units of measurement, and other uniquely Chinese terms such as the Five Elements and the Bagua. During translation, it is often challenging to find exact lexical equivalents for these names in the target language, necessitating the use of phonetic transliteration. Phonetic transliteration is a common method in the translation of Chinese classics. However, improper use of phonetic transliteration can present reading difficulties for readers, impede subsequent cultural exchange, and is particularly problematic in works like The Classic of Tea, which require extensive use of this method. This paper examines the application of phonetic transliteration in the English translation of The Classic of Tea from the perspective of reader comprehension. It identifies issues such as excessive use of transliteration, difficulties for Western readers in pronunciation, and ambiguity in reference. To address these issues, strategies such as reducing transliteration, adjusting information layout, re-annotating phonetically, adding Chinese characters, and inserting hyperlinked audio files are proposed. These strategies aim to alleviate the reading burden and enhance the effectiveness of phonetic transliteration.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.

Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review.

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.

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

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

A Smart Ski Pole for Skiing Pattern Recognition and Quantification Application.

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier's technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier's pole force, pole angle, inertia data, etc., and the system's design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique.