Accurate Motion Detection Research Articles

High-Performance Flexible PLA/BTO-Based Pressure Sensor for Motion Monitoring and Human-Computer Interaction.

Flexible electronics show wide application prospects in electronic skin, health monitoring, and human-machine interfacing. As an essential part of flexible electronics, flexible pressure sensors have become a compelling subject of academic research. There is an urgent need to develop piezoelectric sensors with high sensitivity and stability. In this work, the high flexibility of polylactic acid (PLA) film and the excellent ferroelectric properties and high dielectric constant of tetragonal barium titanate (BTO) led to their use as filling materials to fabricate flexible piezoelectric composite films by spinning coating. PLA is used to produce flexible binding substrates, and BTO is added to the composite to enhance its electrical output by improving its piezoelectric performance. The peak output voltage of the PLA/BTO tetragonal piezoelectric film is 22.57 V, and the maximum short-circuit current was 3041 nA. Durability tests showed that during 40,000 s of continuous operation, in the range of 15~120 kPa, the linear relationship between pressure and the film was excellent, the sensitivity for the output voltage is 0.176 V/kPa, and the output current is 27.77 nA/kPa. The piezoelectric pressure sensor (PPS) also enables accurate motion detection, and the extensive capabilities of the PENG highlight its potential in advancing motion sensing and human-computer interactions.

DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Behavioural stochastic resonance across the lifespan.

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signalsin non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

Pengembangan Game Terapi Bagi Anak Autisme Berbasis Motion Capture dengan Metode Optimasi Kalman Filter

Children with autism spectrum disorder often face challenges in developing motor skills and social interaction abilities. This research aimed to develop an interactive motion capture-based therapy game that could assist autistic children in practicing their motor skills through an enjoyable play experience. The Kalman Filter optimization method was employed to enhance the accuracy and quality of motion detection in the motion capture process. The development of this therapy game involved consultations with therapists, psychologists, and relevant experts in designing game activities tailored to the needs of autistic children. Motion capture technology was utilized to capture the children's body movements in real-time, while the Kalman Filter algorithm was implemented to filter and optimize the acquired motion data. The game interface was designed to be simple, featuring attractive visual objects, bright colors, and familiar sounds. The testing results demonstrated that the application of the Kalman Filter method could increase motion detection accuracy up to 90%, ensuring a more responsive and immersive play experience for the children. This therapy game proved effective in improving motor skills, body coordination, as well as motivation and engagement of autistic children in the therapy process. This research contributes to the development of an enjoyable and effective interactive therapy solution to assist autistic children in developing essential skills for daily life

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

Motion detection in video streams is important for many applications, such as autonomous navigation, human-computer interaction, and spying. Conventional techniques, which depend on backdrop removal or frame differencing, frequently break down when dealing with intricate motion patterns and occlusions. Current techniques struggle to handle occlusions and correctly capture complex motion patterns. Furthermore, these techniques could be computationally expensive, especially when dealing with big amounts of video data. An integrated strategy combining optical flow estimation with 3D convolutional neural network (3D-CNN) architectures is presented to address these limitations and boost motion detection systems' accuracy and efficiency. The suggested technique is unusual as it combines 3D CNNs with optical flow estimates. Motion vectors representing dynamic scene changes are produced by optical flow estimates, and spatiotemporal characteristics are extracted by 3D CNNs processing together with optical flow information. By using the complementing capabilities of both approaches, the method performs better in motion detection applications such as object tracking, action recognition, and anomaly detection in video streams. The efficiency of the strategy is assessed by experiments carried out on benchmark datasets. The results show that it is more accurate, resistant against occlusions, and computationally efficient than existing approaches. The suggested approach offers a viable way to improve motion detection capabilities for a range of practical uses. The results show a 98% improvement in processing efficiency and motion detection accuracy when compared to baseline methods. More research and advancement in this area are made possible by the attainable way that MATLAB's suggested approach offers to enhance motion detection skills in a variety of real-world applications.

All porous Ecoflex and SEBS-based stretchable high-performance triboelectric nanogenerator for self-powered human activity monitoring

Achieving a high output performance by expanding the surface area of triboelectric materials is key to developing triboelectric nanogenerators (TENGs) for future versatile applications. Herein, highly porous Ecoflex and styrene-ethylene-butylene-styrene (SEBS) using chloroform are introduced to improve TENG performance. The triboelectric output increases significantly with increasing porosity, which can be attributed to the increase in the contact area, dielectric constant, and electrostatic induction in the porous structure, thereby resulting in additional surface charges on the porous surface and increased surface charge density of the friction materials. The TENG can stretch by up to 550 % with the designed fabric electrode configuration. Facilitated by the increased contact area of 3D printed microstructure and porous Ecoflex substrate, the fabricated TENG demonstrates a high voltage of 2280 V, corresponding to a power density of 18.8 W m−2, which is enough to power 460 light-emitting diodes and stopwatches. Demonstration of monitoring the walking steps of patients with Parkinson’s and blind people was also performed, which revealed outstanding sensing capabilities. In addition, the TENG-based self-powered sensor demonstrated an identification accuracy of 99.3 % and a motion detection accuracy of 98.2 % based on a deep learning model. Furthermore, by exploiting the outstanding ultrahigh sensitivity, the TENG was demonstrated for low-frequency football player motion monitoring. Therefore, the results of this study provide new insights into investigating porous materials and an efficient method to construct high-performance TENGs that can promote their practical application in the fields of smart healthcare and sports.

Remotely imaging seismic ground shaking via large-N infrasound beamforming

Seismic ground motion creates low-frequency atmospheric sound (infrasound) that is detectable at remote sensor arrays. However, earthquake infrasound signal analysis is complicated by interference between multiple waves arriving at sensors simultaneously, reducing the accuracy and detail of ground motion detection. Here we show that individual waves in complicated wavefields can be resolved by recording infrasound on large-N arrays and processing with CLEAN beamforming. Examining both a local (ML3.5, purely tropospheric infrasound propagation) and regional earthquake (ML6.5, upper-atmospheric returns), we detect infrasound from tens of km away and up to several hundred km away respectively. Source regions span arcs of approximately 90°, indicating that although detection bias does occur (most likely from atmospheric winds) the recorded infrasound sources are widely dispersed and not simply epicentral. Infrasound-based remote detection of ground motion over wide areas can complement point measurements by seismometers and spur innovations in earthquake research and real-time hazard monitoring.

Inertial Measurement Unit-Based Real-Time Adaptive Algorithm for Human Walking Pattern and Gait Event Detection

In this work, a lightweight adaptive hybrid gait detection method with two inertial measurement units (IMUs) on the foot and thigh was developed and preliminarily evaluated. An adaptive detection algorithm is used to eliminate the pre-training phase and to modify parameters according to the changes within a walking trial using an adaptive two-level architecture. The present algorithm has a two-layer structure: a real-time detection algorithm for detecting the current gait pattern and events at 100 Hz., and a short-time online training layer for updating the parameters of gait models for each gait pattern. Three typical walking patterns, including level-ground walking (LGW), stair ascent (SA), and stair descent (SD), and four events/sub-phases of each pattern, can be detected on a portable Raspberry-Pi platform with two IMUs on the thigh and foot in real-time. A preliminary algorithm test was implemented with healthy subjects in common indoor corridors and stairs. The results showed that the on-board model training and event decoding processes took 20 ms and 1 ms, respectively. Motion detection accuracy was 97.8% for LGW, 95.6% for SA, and 97.1% for SD. F1-scores for event detection were over 0.86, and the maximum time delay was steadily below 51 ± 32.4 ms. Some of the events in gait models of SA and SD seemed to be correlated with knee extension and flexion. Given the simple and convenient hardware requirements, this method is suitable for knee assistive device applications.

A Self‐Powered and Self‐Sensing Lower‐Limb System for Smart Healthcare

AbstractIn the age of the artificial intelligence of things (AIoT), wearable devices have been extensively developed for smart healthcare. This paper proposes a self‐powered and self‐sensing lower‐limb system (SS‐LS) with negative energy harvesting and motion capture for smart healthcare. The SS‐LS achieves self‐sustainability via a half‐wave electromagnetic generator (HW‐EMG) that recovers negative work from walking with a low cost of harvesting. Additionally, the motion capture function of the system is achieved by the three‐channel triboelectric nanogenerator (TC‐TENG) based on binary code, which can accurately detect the angle and direction of the knee joint rotation. The bench test experiment indicates that the HW‐EMG has an average output power of 11.2 mW, sufficient to power a wireless sensor. The three‐channel voltage signal of TC‐TENG fits well with the binary signal, which can precisely detect the angle and direction of rotation. Furthermore, the SS‐LS demonstrates an identification accuracy of 99.68% and a motion detection accuracy of 99.96% based on an LSTM deep learning model. Demonstrations of Parkinson's disease and fall detection and monitoring of three training modes (sit‐and‐stand, balance, and walking training) are also performed, which exhibit outstanding sensing capabilities. The SS‐LS is highly promising in sports rehabilitation medicine and can contribute to the development of smart healthcare.

Detection method of limb movement in competitive sports training based on deep learning

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.

Multimodal health monitoring via a hierarchical and ultrastretchable all-in-one electronic textile

Wearable electronics with conductive yet stretchable bio-interfaces, multimodal health regulating capabilities, and strong weather resistance are especially expected by athletes and outdoor workers, yet conventional rigid, impermeable, and monofunctional wearables can hardly meet these requirements simultaneously. Here, a hierarchical and ultrastretchable all-in-one health-regulating electronic textile (AIO E-textile) with robust superhydrophobic and antibacterial surfaces is developed through electrospin-assisted layer-by-layer assembly and liquid metal (LM) printing. Skin-like and bio-compatible styrene-ethylene-butylene-styrene (SEBS) nonwoven textile is employed as a breathable matrix for the layered E-textile, in which an electrophysiological recording circuit, a strain-sensing circuit, and a Joule heating circuit are successively patterned using EGaIn. To ensure human skin health and weather resistance of the device, antibacterial and waterproof surfaces are respectively installed on both sides of the electronic textile via anchoring functionalized nanoparticles. Thus, this ultrastretchable (1141 %) AIO E-textile shows favorable moisture permeability (1300 g m−2 day), exceptional water repellency, remarkable antibacterial efficacy (>99.99 %) against E-coli, and low-watt Joule heating ability, which even exhibits accurate under-water motion detection capability, and high-definition acquiring of bioelectrical signals (electrocardiogram (ECG) and surface electromyography (sEMG)) when used as flexible epidermal bioelectrode. This work opens up a new avenue for daily applicable and multifunctional on-skin electronics.

Motion Recognition Method for Construction Workers Using Selective Depth Inspection and Optimal Inertial Measurement Unit Sensors

The construction industry holds the worst safety record compared to other industrial sectors, and approximately 88% of accidents result in worker injury. Meanwhile, after the development and wide application of deep learning in recent years, image processing has greatly improved the accuracy of human motion detection. However, owing to equipment limitations, it is difficult to effectively improve depth-related problems. Wearable devices have also become popular recently, but because construction workers generally work outdoors, the variable environment makes the application of wearable devices more difficult. Therefore, reducing the burden on workers while stabilizing the detection accuracy is also an issue that needs to be considered. In this paper, an integrated sensor fusion method is proposed for the hazard prevention of construction workers. First, a new approach, called selective depth inspection (SDI), was proposed. This approach adds preprocessing and imaging assistance to the ordinary depth map optimization, thereby significantly improving the calculation efficiency and accuracy. Second, a multi-sensor-based motion recognition system for construction sites was proposed, which combines different kinds of signals to analyze and correct the movement of workers on the site, to improve the detection accuracy and efficiency of the specific body motions at construction sites.

Development of an Early Fire Detection Technique Using a Passive Infrared Sensor and Deep Neural Networks

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively.

Real-Time Monitoring of College Sports Dance Competition Scenes Using Deep Learning Algorithms

In order to improve the real-time detection effect, therefore, a research on real-time scene detection of sports dance competition based on deep learning is proposed. The collected scene image is grayed by using the weighted average method, and the best image interpolation is calculated by using the deep learning method, so as to realize the smooth processing of sawtooth and mosaic information generated by panoramic mapping. After selecting the cube model, the processed scene information is projected to the visual plane to construct the panorama of the competition scene. Finally, combined with the three-frame difference, the changes between adjacent image frames are calculated to obtain the moving target. The test results show that the motion detection accuracy of professional dancers can reach more than 75.0% and that of amateur dancer can reach more than 64.2%.

Mechanically toughened conductive hydrogels with shape memory behavior toward self-healable, multi-environmental tolerant and bidirectional sensors

• A toughened, self-healing, anti-freezing, anti-drying, and anti-swelling hydrogel is prepared. • Hydrogel sensors exhibit excellent self-healing capability and multi-environmental stability. • Hydrogel sensors display an attracting feature on distinguishing the direction of human motion. • Hydrogels possess significant shape memory and information storage functions even at −40 °C. Conductive hydrogels have considered as promising materials to revolutionize flexible electronics, biomedical devices and soft robotics. Despite significant progress has been made in the design of hydrogels, simultaneous realization of outstanding mechanical properties, self-healing capability, multi-environmental tolerance (e.g., freezing, drying, water, and oil), and multiple stimuli-responsiveness in a single hydrogel still remains a tremendous challenge. Herein, an extraordinary stretchable (1769%), toughened (0.46 MPa), self-healing, water-retaining (78% after 8 days), anti-freezing (below −40 °C), and anti-swelling (in water/organic solvents) conductive hydrogel is designed and prepared by simultaneously introducing zwitterionic proline, metal ions, and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) into vinyl hybrid silica nanoparticles cross-linked polyacrylic acid networks. Based on this hydrogel, the fabricated strain sensor exhibits excellent strain sensitivity (GF = 3.07), wide sensing range (from 5 to 700%), reliable stability (800 cycles at 200% strain), and accurate human motion detection along two opposite directions (bend down or bend up). In particular, it can not only withstand outstanding mechanical damages but also maintain stable sensing performances in water/organic solvent environments and freezing condition of −40 °C. More interestingly, under FeCl 3 -EG (ethylene glycol) and ascorbic acid-EG solution external stimuli, the resultant hydrogels also display the significant shape memory behaviors and repeated information recording/erasing capabilities even at an extremely cold temperature (−40 °C). These remarkable performances greatly expand the application fields of hydrogels in harsh environments.

Effects of position-triggered electrical stimulation on poststroke hemiparetic shoulder subluxation.

Shoulder subluxation is a frequent complication after stroke causing joint instability, shoulder pain, decreased activities of daily living, and impedance to rehabilitation progress. Electrical stimulation (ES) is considered an effective modality to reduce shoulder subluxation in acute stroke. However, few studies have investigated the effect of position-triggered ES, which induces active muscle contraction though accurate motion detection. The aim of this study was to investigate whether position-triggered ES was more effective in reducing acute hemiplegic shoulder subluxation after stroke than passive ES. Single-blind, randomized controlled trial. The study setting was the university hospital rehabilitation center. Fifty poststroke subacute hemiparetic patients with shoulder subluxation. Patients were randomly assigned into two groups. The position-triggered ES group received 30-minute ES sessions, 5 days per week for 3 weeks with specially modified Novastim® CU-FS1 (CU Medical Systems, Inc., Gangwon-do, South Korea) for motion triggering. The passive ES group received the same protocol without motion triggering. The vertical distance (VD) and the joint distance (JD), relative VD and JD (rVD, rJD), upper extremity component of Fugl-Meyer Motor Assessment (FMA<inf>upper</inf>), Motricity Index (MI), Manual Function Test (MFT), and peak torque of affected shoulder abductor (PT) were assessed at baseline (T0), end of electrical stimulation session (T1), and 3 weeks (T2) after treatment. Repeated-measures analysis of variance revealed significant interaction between time and intervention on JD and rJD, indicating that shoulder subluxation was significantly more reduced in position-triggered ES than in passive ES (P<0.05). However, FMA<inf>upper</inf>, MI, MFT, and PT did not show this significance. The change of (∆)JD, ∆rVD, and ∆rJD in the motion-triggered ES group improved significantly more at T1 than in the passive ES group (P<0.05). This significant improvement was not seen at T2. Position-triggered ES may be more effective than passive ES in improving poststroke shoulder subluxation; however, this effect was not maintained after the withdrawal of stimulation. Position-triggered ES may be useful to reducing poststroke shoulder subluxation.

High resolution screen-printing of carbon black/carbon nanotube composite for stretchable and wearable strain sensor with controllable sensitivity

With the growing trend of accelerating sensor miniaturization, high-resolution printing technology of stretchable sensors is becoming increasingly important. In this study, we demonstrated stretchable strain sensor with various line resolutions from 50 to 500 μm based on CB/CNT composite using screen printing process for human motion detection. To optimize composite formula, we systematically investigated rheological properties of the composites, resulting in realization of screen printed pattens with 50–500 μm of line width; and also investigated the relation among printed line width, resistance, conductivity, and line thickness. The printed pattern elastic strain sensor exhibited a controllable gauge factor (G.F.) such as 2.6 at 5% strain and 4.1 at 50% strain for linewidths of 50 and 500 μm, respectively, for accurate human motion detection and stable performance was also detected for 300 bending tests. From the result of this study, we proved that the printed pattern can be a potential stretchable strain sensor with controllable sensitivity and high resistance to temperature change.

Instrumental Validity of the Motion Detection Accuracy of a Smartphone-Based Training Game.

Demographic changes associated with an expanding and aging population will lead to an increasing number of orthopedic surgeries, such as joint replacements. To support patients’ home exercise programs after total hip replacement and completing subsequent inpatient rehabilitation, a low-cost, smartphone-based augmented reality training game (TG) was developed. To evaluate its motion detection accuracy, data from 30 healthy participants were recorded while using the TG. A 3D motion analysis system served as reference. The TG showed differences of 18.03 mm to 24.98 mm along the anatomical axes. Surveying the main movement direction of the implemented exercises (squats, step-ups, side-steps), differences between 10.13 mm to 24.59 mm were measured. In summary, the accuracy of the TG’s motion detection is sufficient for use in exergames and to quantify progress in patients’ performance. Considering the findings of this study, the presented exer-game approach has potential as a low-cost, easily accessible support for patients in their home exercise program.

Mechanical characterization of abdominal aortas using multi-perspective ultrasound imaging

Mechanical characterization of abdominal aortic aneurysms using personalized biomechanical models is being widely investigated as an alternative criterion to assess risk of rupture. These methods rely on accurate wall motion detection and appropriate model boundary conditions. In this study, multi-perspective ultrasound is combined with finite element models to perform mechanical characterization of abdominal aortas in volunteers.Multi-perspective biplane radio frequency ultrasound recordings were made under seven angles (-45° to 45°) in one phantom set-up and eight volunteers, which were merged using automatic image registration. 2-D displacement fields were estimated in the seven longitudinal ultrasound views, creating a sparse, high resolution 3-D map of the wall motion at relatively high frame rates (20–27 Hz). The displacements were used to personalize the subject-specific finite element model of which the geometry of the aorta, spine, and surrounding tissue were determined from a single 3-D ultrasound acquisition.Automatic registration of the multi-perspective images was successful in six out of eight cases with an average error of 5.4° compared to the ground truth. Displacements of the aortic wall were measured and cyclic strain of the aortic diameter was found ranging from 4.2% to 8.6%. The subject-specific mesh and inverse FE analysis was performed yielding shear moduli estimates for the wall between 104 and 215 kPa. Comparative results from a single-perspective workflow revealed very low aortic wall motion signal, which resulted in relatively high modulus estimates, between 230 and 754 kPa.Multi-perspective biplane ultrasound imaging was used to personalize finite element models of the abdominal aorta and its surroundings, and performing mechanical characterization of the aortic shear modulus. The method was found to be a more robust method compared to a single-perspective 3-D ultrasound approach. Future research will focus on investigating the use of multiple 3-D ultrasound acquisitions, the feasibility of free-hand scanning, the creation of a full 3-D automatic registration process, and with that, enable a clinical continuation of this study.

Technical Note: Comprehensive performance tests of the first clinical real-time motion tracking and compensation system using MLC and jaws.

PurposeTo evaluate the performance of the first clinical real‐time motion tracking and compensation system using multileaf collimator (MLC) and jaws during helical tomotherapy delivery.MethodsAppropriate mechanical and dosimetry tests were performed on the first clinical real‐time motion tracking system (Synchrony on Radixact, Accuray Inc) recently installed in our institution. kV radiography dose was measured by CTDIw using a pencil chamber. Changes of beam characteristics with jaw offset and MLC leaf shift were evaluated. Various dosimeters and phantoms including A1SL ion chamber (Standard Imaging), Gafchromic EBT3 films (Ashland), TomoPhantom (Med Cal), ArcCheck (Sun Nuclear), Delta4 (ScandiDos), with fiducial or high contrast inserts, placed on two dynamical motion platforms (CIRS dynamic motion‐CIRS, Hexamotion‐ScandiDos), were used to assess the dosimetric accuracy of the available Synchrony modalities: fiducial tracking with nonrespiratory motion (FNR), fiducial tracking with respiratory modeling (FR), and fiducial free (e.g., lung tumor tracking) with respiratory modeling (FFR). Motion detection accuracy of a tracking target, defined as the difference between the predicted and instructed target positions, was evaluated with the root mean square (RMS). The dose accuracy of motion compensation was evaluated by verifying the dose output constancy and by comparing measured and planned (predicted) three‐dimensional (3D) dose distributions based on gamma analysis.ResultsThe measured CTDIw for a single radiograph with a 120 kVp and 1.6 mAs protocol was 0.084 mGy, implying a low imaging dose of 8.4 mGy for a typical Synchrony motion tracking fraction with 100 radiographs. The dosimetric effect of the jaw swing or MLC leaf shift was minimal on depth dose (<0.5%) and was <2% on both beam profile width and output for typical motions. The motion detection accuracies, that is, RMS, were 0.84, 1.13, and 0.48 mm for FNR, FR, and FFR, respectively, well within the 1.5 mm recommended tolerance. Dose constancy with Synchrony was found to be within 2%. The gamma passing rates of 3D dose measurements for a variety of Synchrony plans were well within the acceptable level.ConclusionsThe motion tracking and compensation using kV radiography, MLC shifting, and jaw swing during helical tomotherapy delivery was tested to be mechanically and dosimetrically accurate for clinical use.