Sensor System For Monitoring Research Articles

Advancements in Sensor Technologies and Control Strategies for Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review.

Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments.

Reducing New and Worsening Pressure Injuries Utilizing an Interface Sensor Monitoring System

Reducing New and Worsening Pressure Injuries Utilizing an Interface Sensor Monitoring System

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Design of an Internet of Things (IoT)-Based Photosynthetically Active Radiation (PAR) Monitoring System

Photosynthetically Active Radiation (PAR) is an important parameter in the plant photosynthesis process, which can relate to plant growth, crop water use, and leaf gas exchange. Previously, many researchers utilized commercially available sensors to monitor PAR. The high cost of the commercially available PAR sensors has limited researchers, agricultural professionals, and farmers to use and expand PAR monitoring in agricultural lands. Thus, this paper focuses on designing an affordable Internet of Things (IoT)-based PAR sensor monitoring system including 3D-printed enclosures (waterproof) for the sensors, performance evaluation of multiple light sensors, solar powering configuration, cloud setup, and cost analysis. Three sensors, including VTB8440BH photodiode, SI 1145, and LI-190R sensors, were evaluated. The 3D-printed waterproof enclosures were designed for the photodiode and SI 1145. Particle Boron was used for recording and sending the sensor data to the IoT webserver. Both the photodiode and SI 1145 were compared to LI-190R, which is the industry standard. In the calibration process, the R2 values of the photodiode and SI 1145 with LI-190R were 0.609 and 0.961, respectively. Field validation data shows that SI 1145 had a strong correlation with LI-190R. In addition, the performance evaluation data shows the photodiode had a weaker correlation with LI-190R than SI 1145. In conclusion, the study successfully developed and designed affordable and reliable IoT-based PAR sensor monitoring systems, including a 3D-printed housing, hardware, programming, and IoT website. SI 1145 with a glass filter is an alternative sensor to monitor PAR at a low cost and has the advantage of being connected to IoT microcontrollers.

Laboratory robustness validation of a humidity sensor system for the condition monitoring of grease-lubricated components for railway applications

Abstract. The condition monitoring of the health status of lubricating greases used in axle box bearings can be realized by applying well-established electrical or optical measurement principles. Furthermore, some novel methods have been reported that make use of humidity sensors or of dielectric thermoscopy. One of the most important grease condition parameters is the water content of the lubricating grease, as water can degrade grease to the point that it is no longer able to provide suitable lubrication and can also damage the bearing due to corrosion and cavitation. In this study, a new approach for water detection in lubricated wagon components is presented that is based on commercially available humidity sensors. The core element of this sensor system is a robust humidity sensor mounted in the immediate atmosphere of the grease-lubricated wagon axle bearing. In the case of water intake, the humidity of the gaseous atmosphere above the grease increases and can be detected by the customized sensor concept Humidity Sensor in Axle Bearings (HSAB). As this sensor system has to be sufficiently robust, it must be able to withstand environmental impact factors. The most important of these factors are temperature, relative humidity, and mechanical load, like vibrations and shocks, depending on the relevant railway application. To mimic these field effects under controlled laboratory conditions, the “lab-to-field” approach was set up and employed. Of the utmost importance was the installation of a development environment for the sensors that enabled the transfer of laboratory results to the respective rail field application. As a result, the HSAB system shows promise with respect to enhancing the reliability of railway wagons and decreasing maintenance costs, thereby reducing the downtime of railway wagons significantly.

Research on Residual-Current Measurement System of Substation Considering Magnetic Shielding Effect.

Residual current is an important monitoring quantity of a power system, and a current sensor plays an important role in detecting current. The substation environment is complex. In addition to the power frequency signal, residual current also has AC and DC components. But it is also affected by the stray magnetic field of the substation. Therefore, the accuracy of the current sensor demands higher requirements. The tunnel magnetoresistive sensor has the advantages of a stable operation, high efficiency, and energy saving, but it is easily affected by the external stray magnetic field during measurements, resulting in a large error. Therefore, this paper proposes a residual-current sensing monitoring system considering the magnetic shielding effect. The root mean square error of the magnetic shielding structure is only 0.572 mA, which can effectively reduce the influence of the external magnetic field and improve the detection accuracy. At the same time, the DC measurement error is less than 1%, the AC measurement error is less than 5%, and the hybrid AC/DC error is less than 8%. It has good response ability and can accurately detect residual current.

Monolithic integrated light-emitting-diode/photodetector sensor for photoactive analyte monitoring: design and simulation.

We present the simulation and design optimization of an integrated light-emitting-diode/photodetector (LED-PD) sensor system for monitoring of light absorbance changes developing in analyte-sensitive compounds. The sensor integrates monolithically both components in a single chip, offering advantages such as downsizing, reduced assembly complexity, and lower power consumption. The changes in the optical parameters of the analyte-sensitive ink are detected by monitoring the power transmission from the LED to the PD. Ray tracing and coupled modeling approach (CMA) simulations are employed to investigate the interaction of the emitted light with the ink. In highly absorbing media, CMA predicts more accurate results by considering evanescent waves. Simulations also suggest that an approximately 39% change in optical transmission can be achieved by adjusting the ink-deposited layer thickness and varying the extinction coefficient from 10-4 to 3×10-4.

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis.

In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method's advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

MONITORING IN VITRO AND EX VIVO INFLAMMATION USING SINGLE-WALLED CARBON NANOTUBES (SWCNTS) SENSOR TECHNOLOGY

Osteoarthritis (OA) is a degenerative joint disease affecting millions worldwide. Early detection of OA and monitoring its progression is essential for effective treatment and for preventing irreversible damage. Although sensors have emerged as a promising tool for monitoring analytes in patients, their application for monitoring the state of pathology is currently restricted to specific fields (such as diabetes). In this study, we present the development of an optical sensor system for real-time monitoring of inflammation based on the measurement of nitric oxide (NO), a molecule highly produced in tissues during inflammation.Single-walled carbon nanotubes (SWCNT) were functionalized with a single-stranded DNA (ssDNA) wrapping designed using an artificial intelligence approach and tested using S-nitroso-N-acetyl penicillamine (SNAP) as a standard released-NO marker. An optical SWIR reader with LED excitation at 650 nm, 730 nm and detecting emission above 1000 nm was developed to read the fluorescence signal from the SWCNTs. Finally, the SWCNT was embedded in GelMa to prove the feasibility of monitoring the release of NO in bovine chondrocyte and osteochondral inflamed cultures (1–10 ng/ml IL1β) monitored over 48 hours. The stability of the inflammation model and NO release was indirectly validated using the Griess and DAF-FM methods. A microfabricated sensor tag was developed to explore the possibility of using ssDNA-SWCNT in an ex vivo anatomic set-up for surgical feasibility, the limit of detection, and the stability under dynamic flexion.The SWCNT sensor was sensitive to NO in both in silico and in vitro conditions during the inflammatory response from chondrocyte and osteochondral plug cultures. The fluorescence signal decreased in the inflamed group compared to control, indicating increased NO concentration. The micro-tag was suitable and stable in joints showing a readable signal at a depth of up to 6 mm under the skin.The ssDNA-SWCNT technology showed the possibility of monitoring inflammation continuously in an in vitro set-up and good stability inside the joint. However, further studies in vivo are needed to prove the possibility of monitoring disease progression and treatment efficacy in vivo.Acknowledgments: The project was co-financed by Innosuisse (grant nr. 56034.1 IP-LS)

Using photodiodes and statistical evaluation methods for process monitoring tasks during the laser beam welding of stainless-steel foils

Besides battery-based mobility solutions, polymer electrolyte membrane fuel cells (PEMFCs) can be utilized to electrify powertrains. Metallic bipolar plates, usually consisting of two thin micro-formed metal foils, are a crucial component of PEMFCs. For the joining of the foils, laser beam welding is the state-of-the-art manufacturing process. Within the scope of this work, a stable parameter window for the welding of AISI 316L was identified. Therefore, an adjustable ring-mode laser beam source in combination with a photodiode-based sensor system for process monitoring was applied. During the experiments, artificial process faults (e.g., contamination of the samples) were introduced to understand their impact on the welding process and the photodiode signals. The welded samples were evaluated using metallographic cross-sections and surface topography scans. Finally, a statistical signal analysis was employed to interpret the data obtained from the photodiodes to distinguish between fault-free and defective weld seams based on specific signal features.

Evaluation of association between time in range, a continuous glucose monitoring metric, and cardiac autonomic neuropathy in type 2 diabetes patients.

Time in range (TIR), a metric of continuous glucose monitoring (CGM) provides better information regarding the individual's glycemic variability than a static measure like glycated hemoglobin (HbA1c). TIR is emerging as an independent risk factor for diabetic complications, both microvascular and macrovascular complications independent of HbA1c. Hence, this study evaluates the association between TIR and cardiac autonomic neuropathy (CAN) in type 2 diabetic patients. A total of 42 patients with type 2 diabetes mellitus were enrolled in this study and underwent a 3-day CGM using the "FreeStyle Libre Pro Flash Glucose Monitoring System Sensor" along with tests for CAN within the 3 days of attaching the CGM. Out of 42 patients, 36 patients (85.7%) were diagnosed with CAN (early CAN 57.1% and definite CAN 28.6%) and the mean TIR was 64.4% ±23.5%. Out of those with TIR <70%, 42.9% were affected with definite CAN compared to only 14.3% among those with TIR >70%. Patients with more severe CAN were found to have a lower TIR (P = 0.115). The study found a high prevalence of cardiac autonomic neuropathy (CAN) of around 85.7% in type 2 diabetes patients. Lower TIR values were associated with a higher incidence of definite CAN (42.9% vs. 14.3% in TIR <70% vs. >70% groups). The findings suggest TIR is inversely associated with the presence and severity of cardiac autonomic neuropathy in type 2 diabetic patients and also a potential link between TIR and CAN severity.

All-Weather Monitoring of Ulva prolifera in the Yellow Sea Based on Sentinel-1, Sentinel-3, and NPP Satellite Data

Ulva prolifera (U. prolifera), a global eco-environmental issue, has been recurring annually in the Yellow Sea of China since 2007, leading to significant impacts on the coastal ecosystem and the economies of coastal cities. To enhance the frequency of daily monitoring for U. prolifera and to advance the multi-source remote sensing monitoring system, a combination of the Sentinel-1 SAR remote sensing satellite and the Sentinel-3 OLCI and NPP VIIRS optical remote sensing satellites was employed. This comprehensive analysis encompassed the examination of Sentinel-1 C band characteristics, the range of influence of U. prolifera, and the migration trajectory of its enrichment zones. On 6 June 2021, three satellite images depicted the northwest drift of U. prolifera, followed by a southward movement after making contact with the coast of Qingdao, China, on 12 June. The most extensive impact area caused by U. prolifera was observed on 18 June. Subsequently, the images revealed a contraction and enrichment of U. prolifera in an eas–-west direction. The amalgamation of radar and optical remote sensing satellites in a multi-frequency monitoring approach allows for a continuous all-weather surveillance mechanism for U. prolifera. This mechanism serves to provide timely alerts for the prevention and management of U. prolifera outbreaks.

Analysis of Surface Roughness during Surface Polishing of ITO Thin Film Using Acoustic Emission Sensor Monitoring

This study investigates the intricate process of surface polishing for ITO-coated Pyrex glass utilizing magnetic abrasive polishing (MAP) while employing acoustic emission (AE) sensors for real-time defect monitoring. MAP, known for its versatility in achieving nanoscale thickness processing and uniform surfaces, has been widely used in various materials. However, the complexity of the process, influenced by multiple variables like cutting conditions, material properties, and environmental factors, poses challenges to maintaining high surface quality. To address this, a sensor monitoring system, specifically one that uses AE sensors, was integrated into the MAP process to detect and confirm defects, providing real-time insights into machining conditions and outcomes. AE sensors excel in identifying material deterioration, microcrack formation, and wear, even in cases of minor damage. Leveraging AE sensor data, this study aims to minimize surface defects in ITO thin films during MAP while optimizing surface roughness. The investigation involves theoretical validation, magnetic density simulations, and force sensor pressure measurements to identify factors influencing surface roughness. ANOVA analysis is employed to determine optimal processing conditions. Additionally, this study compares the identified optimal roughness conditions with those predicted by AE sensor parameters, aiming to establish a correlation between predicted and achieved surface quality. The integration of AE sensor monitoring within the MAP process offers a promising avenue for enhancing surface quality by effectively identifying and addressing defects in real time. This comprehensive analysis contributes to advancing the understanding of surface polishing methodologies for ITO-coated Pyrex glass, paving the way for improved precision and quality in thin-film surface processes.

Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Mega on Agricultural Spraying Machines

The focus of the research is monitoring data from sensors on the agricultural sprayer. The monitoring system support by some sensors in camera, tank capacity, boom sprayer balance and battery capacity. The research method was carried out using the waterfall model, because according to the needs that require a sequential flow in the process. This model is divided into four parts, namely analysis (to identify problems and needs), design (plans to solve problems to be solved), implementation (implementation of plans that have been made), and testing. Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Uno on Agricultural Spraying Machines. The test results for the CNN model for the detection of the “Jajar Legowo” object were carried out to obtain 90% accuracy, 82.35% precision and 100% recall. Tests an accuracy value cappacity tank of 100%. Testing the balance sensor, if rotates clockwise on the Y axis the output voltage decreases, and vice versa. However, if the sensor at rest, the output voltage will same as the offset value. Besides that, testing the optimum PWM value fuzzy approach is carried out with aim that the droplets hit the target zone when sprayer is working. The result are Arduino IDE and Matlab produce same value, which is 42 for the optimum PWM value. Testing the battery capacity sensor get accuracy value of 100% by difference in the voltage increase of 0.5 volts is equivalent to increase of 10%. All information read by the sensors is displayed on the LCD using WMS-2000 (smart wireless). Keyword: Fuzzy, Microcontroller, Monitoring, Sensor data, Smart wireless

Fluorometric Sensing of Metal Ions by Carbon Nano Dots - A Review

abstract: Contamination from heavy metal ions is a rising problem. It has direct and indirect impacts on human health, the environment, and aquatic life. Thus, detecting trace amounts of these elements presents significant challenges. It is important to develop effective, affordable, accurate, fast sensing monitoring systems. A new field of sensing techniques has emerged with the discovery of bio-compatible fluorescent carbon nanodots. Although the study of heavy metal ion detection using carbon nanodots and fluorometric sensing techniques is relatively new and developing, it holds great potential for aiding scientists in the development of environmentally friendly sensing systems. This article focuses on the utilization of carbon nanodots in fluorescencebased detection of metal ions in various media. The ability to emit fluorescence across the entire visible spectrum under UV and visible light excitation offers fluorescent quenching or fluorescent enhancement phenomena in the presence of trace amount of metal ions. The present work discusses different fluorometric sensing phenomena, its mechanism and applications of carbon dots on it. It covers the recent progress in carbon-nano dots for bio-based synthesis, physical properties, and application in heavy metal sensing.

Wireless sensor system for real-time performance monitoring in sports

In Paralympic sports, investigating seating ergonomics and optimizing for performance is crucial due to individual impairments. Usually, experiments are conducted in laboratory environments and for skiing, usually on a treadmill. In this paper, we are moving experiments out of the laboratory setting to in-slope performance monitoring of kinetics and kinematics. A wireless sensor system is developed and validated in terms of delay. The results show a median delay of 52 ms for the wired main system and 53 ms for the wireless sub-system. The sensor system was implemented on a highly adjustable Paralympic sit-ski, and an experiment was conducted to pinpoint optimal equipment settings for an individual athlete. In addition, the system provided force data from both knees, seat, belt, and both poles. The data collected can also be used to analyze the technique, in addition to assisting in the classification process in the LW10–12 class. The proposed system design also allows for adding a vast amount of different sensor types, and by testing for delay, synchronized with well-known GNSS and IMU sensors already used in many sports to analyze athlete performance.

Robust in-vehicle respiratory rate detection using multimodal signal fusion

Continuous health monitoring in private spaces such as the car is not yet fully exploited to detect diseases in an early stage. Therefore, we develop a redundant health monitoring sensor system and signal fusion approaches to determine the respiratory rate during driving. To recognise the breathing movements, we use a piezoelectric sensor, two accelerometers attached to the seat and the seat belt, and a camera behind the windscreen. We record data from 15 subjects during three driving scenarios (15 min each) city, highway, and countryside. An additional chest belt provides the ground truth. We compare the four convolutional neural network (CNN)-based fusion approaches: early, sensor-based late, signal-based late, and hybrid fusion. We evaluate the performance of fusing for all four signals to determine the portion of driving time and the signal combination. The hybrid algorithm fusing all four signals is most effective in detecting respiratory rates in the city (P = 62.42), highway (P = 62.67), and countryside (P = 60.94). In summary, 60% of the total driving time can be used to measure the respiratory rate. The number of signals used in the multi-signal fusion improves reliability and enables continuous health monitoring in a driving vehicle.

Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring

This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.&#x0D; The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.&#x0D; However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.&#x0D; There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.&#x0D; The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.&#x0D; By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.&#x0D; Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.&#x0D; This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.

Measuring and Energizing Sensor System for Digital Signal Monitoring of an Academic-Experimental CubeSat for Wireless Telemetry Purposes.

Space technology for small satellites has made significant progress in the academic and industrial fields, and an alternative focused on educational institutions is the CubeSat standard, created to promote various scientific projects of space exploration. In this context, a fundamental module of any satellite is the telemetry subsystem, which controls the communication with the Earth through electronic circuits dedicated to remote communication; also, the measurement and power supply modules are integrated into a CubeSat. Its construction costs range from USD 2500 to 55,000, with suppliers from Europe and the United States. This motivates the development of the present project, aimed at an academic-experimental CubeSat-1U prototype, to limit this technological dependence, focusing on the measurement generated by the acceleration sensors, angular velocity, magnetic fields, barometric pressure, temperature and ultraviolet light intensity, and the energization of each of them. For this, the main objective of the research is to identify the four basic subsystems of the CubeSat-1U: (a) energization subsystem, (b) sensing subsystem, (c) transmission and reception subsystem, and (d) control subsystem. To describe in detail the construction of (a) and (b), a set of diagrams is performed, defining their operation and its interaction. To explain the subsystem's construction, the components selection and integration are presented. As a result, the electrical measurements generated by the power system, the output of the sensors in laboratory conditions, and images of the developed circuits are presented, having as a contribution to the methodology of design, integration, and development of the four subsystems, the feasibility of construction and its implementation in an academic satellite.

Association between a pyroelectric infrared sensor monitoring system and a 3-dimensional accelerometer to assess movement in preweaning dairy calves

The objective of this study was to correlate movement assessed by a pyroelectric infrared sensor system in preweaning dairy calves with lying and standing time assessed by a 3D accelerometer considering the temperature-humidity index (THI). A total of 35 dairy calves (1–7 d of age) were enrolled in the study and 20 calves were included in the final analyses. The lying and standing time of the calves was monitored with a 3D accelerometer (Hobo Pendant G Data Logger, Onset Computer Corporation, USA), which was used as the gold standard reference. The infrared sensor monitoring system (IMS; Calf Monitoring System, Futuro Farming GmbH, Germany) was fixed to the fence of the calf hutch within the calf's reach. Temperature-humidity was monitored with 2 validated THI sensors inside and on outside of each calf hutch. Additionally, one THI sensor was located near the calf hutches. The observation period lasted 14 consecutive days. The average standing time assessed by the 3D accelerometer was 13.4 ± 12.7 (mean ± standard deviation) min/h and the average lying time was 46.6 (±12.7) min/h. The median (25th percentile; 75th percentile) number of movements measured by the IMS was 360 (60; 919) movements per hour. Number of movements per hour measured by the IMS was compared with data obtained with a validated 3D accelerometer. The Pearson correlation coefficient between both standing and lying time and the number of movements was r = 0.85 and r = −0.85, respectively. The Pearson correlation coefficients were only slightly influenced by THI (low THI [<68]: r = 0.86; medium THI [68–72]: r = 0.85; high THI [>72]: r = 0.81). Our data show that the number of movements of dairy calves measured by IMS were highly correlated with the chosen gold standard reference method. High THI slightly affects the measurement accuracy of IMS.