Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration.

Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Bifunctional electrocatalytic activity of Fe-embedded biphenylene for oxygen reduction and evolution reactions.

Transition metal single-atom catalysts have attracted great attention because of their great potential applications in the chemical industry. Except for graphene, there are few single-layer materials that can act as substrates to support the dispersive metal atoms. Recently, a biphenylene layer, a new two-dimensional allotrope of graphene, was synthesized in experiments, providing a new substrate layer to fabricate single-atom catalysts (SACs). In this work, we predict three transition metal SACs MN4-biphenylene (M = Fe, Co, and Ni) based on first-principles calculations. The results indicate that FeN4-biphenylene is a promising bifunctional catalyst with low overpotentials for both the oxygen reduction reaction and oxygen evolution reaction, ηORR = 0.11 V and ηOER = 0.27 V. The high catalytic activities are explained by the position of the d-band center of the Fe atom in the biphenylene network and the strength of interaction between FeN4-biphenylene and the reaction intermediates.

ATM Inhibition-Induced ISG15/IFI27/OASL Is Correlated with Immunotherapy Response and Inflamed Immunophenotype.

Immune checkpoint blockade (ICB) therapy can improve the survival of cancer patients with a high tumor mutation burden (TMB-H) or deficiency in DNA mismatch repair (dMMR) in their tumors. However, most cancer patients without TMB-H and dMMR do not benefit from ICB therapy. The inhibition of ATM can increase DNA damage and activate the interferon response, thus modulating the tumor immune microenvironment (TIME) and the efficacy of ICB therapy. In this study, we showed that ATM inhibition activated interferon signaling and induced interferon-stimulated genes (ISGs) in cisplatin-resistant and parent cancer cells. The ISGs induced by ATM inhibition were correlated with survival in cancer patients who received ICB therapy. In oral cancer, high expressions of ISG15, IFI27, and OASL were associated with low expressions of ATM, the activation of inflamed immune pathways, and increased tumor-infiltrating scores of CD8+ T, natural killer, and dendritic cells. The high expressions of ISG15, IFI27, and OASL were also correlated with complete remission in patients with cervical cancer treated with cisplatin. These results suggest that ATM inhibition can induce the interferon response and inflamed TIME, which may benefit ICB therapy.

Analysis of Curative Effect and Prognostic Factors of Radiotherapy for Esophageal Cancer Based on the CNN.

An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency of physicians, as well as to improve the level of esophageal cancer image diagnosis in primary care institutions. In this paper, by collecting medical images related to esophageal cancer over the years, we establish an intelligent diagnosis system based on the convolutional neural network for esophageal cancer images through the steps of data annotation, image preprocessing, data enhancement, and deep learning to assist doctors in intelligent diagnosis. The convolutional neural network-based esophageal cancer image intelligent diagnosis system has been successfully applied in hospitals and widely praised by frontline doctors. This system is beneficial for primary care physicians to improve the overall accuracy of esophageal cancer diagnosis and reduce the risk of death of esophageal cancer patients. We also analyze that the efficacy of radiation therapy for esophageal cancer can be influenced by many factors, and clinical attention should be paid to grasp the relevant factors in order to improve the final treatment effect and prognosis of patients.

Effect of Intensity Modulated Radiotherapy (IMRT) on the immunity, physical status and clinical effect of locally advanced NSCLC patients.

OBJECTIVES: To evaluate the clinical value of radiotherapy combined with Camrelizumab in treating locally advanced non-small cell lung cancer (NSCLC) patients. METHODS: 80 locally advanced NSCLC patients were randomly divided into two groups (n=40). The control group was administered with intensity modulated radiation therapy (IMRT), whereas the experimental group with Camrelizumab in addition to IMRT. All the patients underwent clinical efficacy evaluation in terms of adverse drug reaction (ADR), physical status improvement after the treatment, and changes in T lymphocyte subpopulations (incl. CD3+, CD4+, CD8+, CD4+/CD8+). RESULTS: The efficacy was found to be 70% and 47.5 in experimental group and control group, respectively, with the former being significantly better than the latter (p=0.03). The ADR rates were 50% and 37.5% in the experimental group and control group, respectively; but the difference remained insignificant (p=0.26). As for physical status improvement, experimental group evidently excelled the control group (p=0.04). The post-treatment indicators such as CD3+, CD4+, CD8+, CD4+/CD8+ were significantly more improved in the experimental group than the control group (CD3+, p=0.02; CD4+, p=0.00; and CD4+/CD8+, p=0.01). However, the changes in CD8+ were not significant at all (p=0.46). CONCLUSIONS: The combined therapy of IMRT with Camrelizumab appeared effective in dealing with the locally advanced NSCLC patients, as such patients presented significantly better immune state and physical status improvement but not increased ADR. The therapy is both safe and effective.

IMU-based knee flexion, abduction and internal rotation estimation during drop landing and cutting tasks.

Anterior cruciate ligament (ACL) injury is a common and severe knee injury in sports. Knee flexion, abduction and internal rotation angles are considered crucial biomechanical indicators of the ACL injury risk but currently are computed in a laboratory with an optical motion capture. This paper introduces an inertial measurement unit (IMU) based algorithm for knee flexion, abduction and internal rotation estimation during ACL injury risk assessment tests, including drop landing and cutting tasks. This algorithm includes a special two-step complementary-based orientation filter and a special single-pose sensor-to-segment calibration procedure. Fourteen healthy subjects performed double-leg, single-leg drop landing and cutting tasks. Each subject wore four IMUs and reflective marker clusters on their thighs and shanks. For the presented knee angles algorithm with an empirical initial segment orientation, the root mean square errors (RMSEs) of the estimated continuous knee flexion, abduction and internal rotation cross all the movement tasks were 1.07°, 2.87° and 2.64°, and RMSEs of the peak knee flexion and peak knee abduction errors were 1.22° and 3.82°. The knee angles algorithm was capable of estimating knee abduction and internal rotation angles during drop landing and cutting tasks, and knee flexion estimation was substantially more accurate than previously reported approaches. Additionally, we found that for the presented algorithm, the accuracy of initial segment orientation was a critical factor for knee abduction and internal rotation estimations. The presented IMU-based knee angles algorithm could serve as a foundation to enable in-field biomechanical ACL injury risk assessment.

Feasibility of Wearable Haptic Biofeedback Training for Reducing the Knee Abduction Moment During Overground Walking.

Gait modifications are effective in reducing the first peak knee abduction moment (PKAM), a surrogate for knee loading. Reliance on 3D motion capture currently restricts these modifications to the laboratory. Therefore, our purpose was to test the feasibility of a novel wearable biofeedback system to train (1) toe-in and trunk lean modifications and (2) combined toe-in and trunk lean modifications to reduce PKAM during overground walking outside of the laboratory. Twelve healthy participants practiced modifications in a university hallway directly after performing five normal walking trials. The wearable feedback system provided real-time haptic biofeedback during training trials to inform participants if they were within the prescribed modification range (7-12 deg greater than baseline). Participants were instructed to move to the next modification only once they felt comfortable and could perform it with minimal errors. Following training, five trials of each modification were immediately performed in the gait laboratory without feedback. All participants successfully modified their foot progression and trunk angle using the wearable system. At post-test, PKAM decreased from baseline by 62%, 55%, and 28% during combined, trunk leanand toe-in gait, respectively. The wearable feedback system was effective to modify participants' foot and trunk angle by the prescribed amount, resulting in reduced PKAM during all modifications at post-test. Participants were also able to perform a combined modification, although it took longer to report feeling comfortable doing so. This study demonstrates that a wearable feedback system is feasible to modify kinematic parameters and train gait modifications outside the laboratory.

Lossless Compression of Human Movement IMU Signals.

Real-time human movement inertial measurement unit (IMU) signals are central to many emerging medical and technological applications, yet few techniques have been proposed to process and represent this information modality in an efficient manner. In this paper, we explore methods for the lossless compression of human movement IMU data and compute compression ratios as compared with traditional representation formats on a public corpus of human movement IMU signals for walking, running, sitting, standing, and biking human movement activities. Delta coding was the highest performing compression method which compressed walking, running, and biking data by a factor of 10 and compressed sitting and standing data by a factor of 18 relative to the original CSV formats. Furthermore, delta encoding was shown to approach the a posteriori optimal linear compression level. All methods were implemented and released as open source C code using fixed point computation which can be integrated into a variety of computational platforms. These results could serve to inform and enable human movement data compression in a variety of emerging medical and technological applications.

Vertical Jump Height Estimation Algorithm Based on Takeoff and Landing Identification Via Foot-Worn Inertial Sensing.

Vertical jump height is widely used for assessing motor development, functional ability, and motor capacity. Traditional methods for estimating vertical jump height rely on force plates or optical marker-based motion capture systems limiting assessment to people with access to specialized laboratories. Current wearable designs need to be attached to the skin or strapped to an appendage which can potentially be uncomfortable and inconvenient to use. This paper presents a novel algorithm for estimating vertical jump height based on foot-worn inertial sensors. Twenty healthy subjects performed countermovement jumping trials and maximum jump height was determined via inertial sensors located above the toe and under the heel and was compared with the gold standard maximum jump height estimation via optical marker-based motion capture. Average vertical jump height estimation errors from inertial sensing at the toe and heel were -2.2±2.1 cm and -0.4±3.8 cm, respectively. Vertical jump height estimation with the presented algorithm via inertial sensing showed excellent reliability at the toe (ICC(2,1)=0.98) and heel (ICC(2,1)=0.97). There was no significant bias in the inertial sensing at the toe, but proportional bias (b=1.22) and fixed bias (a=-10.23cm) were detected in inertial sensing at the heel. These results indicate that the presented algorithm could be applied to foot-worn inertial sensors to estimate maximum jump height enabling assessment outside of traditional laboratory settings, and to avoid bias errors, the toe may be a more suitable location for inertial sensor placement than the heel.

Configurable, wearable sensing and vibrotactile feedback system for real-time postural balance and gait training: proof-of-concept.

BACKGROUND: Postural balance and gait training is important for treating persons with functional impairments, however current systems are generally not portable and are unable to train different types of movements. METHODS: This paper describes a proof-of-concept design of a configurable, wearable sensing and feedback system for real-time postural balance and gait training targeted for home-based treatments and other portable usage. Sensing and vibrotactile feedback are performed via eight distributed, wireless nodes or "Dots" (size: 22.5 × 20.5 × 15.0 mm, weight: 12.0 g) that can each be configured for sensing and/or feedback according to movement training requirements. In the first experiment, four healthy older adults were trained to reduce medial-lateral (M/L) trunk tilt while performing balance exercises. When trunk tilt deviated too far from vertical (estimated via a sensing Dot on the lower spine), vibrotactile feedback (via feedback Dots placed on the left and right sides of the lower torso) cued participants to move away from the vibration and back toward the vertical no feedback zone to correct their posture. A second experiment was conducted with the same wearable system to train six healthy older adults to alter their foot progression angle in real-time by internally or externally rotating their feet while walking. Foot progression angle was estimated via a sensing Dot adhered to the dorsal side of the foot, and vibrotactile feedback was provided via feedback Dots placed on the medial and lateral sides of the mid-shank cued participants to internally or externally rotate their foot away from vibration. RESULTS: In the first experiment, the wearable system enabled participants to significantly reduce trunk tilt and increase the amount of time inside the no feedback zone. In the second experiment, all participants were able to adopt new gait patterns of internal and external foot rotation within two minutes of real-time training with the wearable system. CONCLUSION: These results suggest that the configurable, wearable sensing and feedback system is portable and effective for different types of real-time human movement training and thus may be suitable for home-based or clinic-based rehabilitation applications.

Validation of a smart shoe for estimating foot progression angle during walking gait.

The foot progression angle is an important measurement related to knee loading, pain, and function for individuals with knee osteoarthritis, however current measurement methods require camera-based motion capture or floor-embedded force plates confining foot progression angle assessment to facilities with specialized equipment. This paper presents the validation of a customized smart shoe for estimating foot progression angle during walking. The smart shoe is composed of an electronic module with inertial and magnetometer sensing inserted into the sole of a standard walking shoe. The smart shoe charges wirelessly, and up to 160h of continuous data (sampled at 100Hz) can be stored locally on the shoe. For validation testing, fourteen healthy subjects were recruited and performed treadmill walking trials with small, medium, and large toe-in (internal foot rotation), small, medium, and large toe-out (external foot rotation) and normal foot progression angle at self-selected walking speeds. Foot progression angle calculations from the smart shoe were compared with measurements from a standard motion capture system. In general, foot progression angle values from the smart shoe closely followed motion capture values for all walking conditions with an overall average error of 0.1±1.9deg and an overall average absolute error of 1.7±1.0deg. There were no significant differences in foot progression angle accuracy across the seven different walking gait patterns. The presented smart shoe could potentially be used for knee osteoarthritis or other clinical applications requiring foot progression angle assessment in community settings or in clinics without specialized motion capture equipment.

Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking.

BACKGROUND: Increased physical activity can provide numerous health benefits. The relationship between physical activity and health assumes reliable activity measurements including step count and distance traveled. This study assessed step count and distance accuracy for Nike+ FuelBand, Jawbone UP 24, Fitbit One, Fitbit Flex, Fitbit Zip, Garmin Vivofit, Yamax CW-701, and Omron HJ-321 during level, upstairs, and downstairs walking in healthy adults. METHODS: Forty subjects walked on flat ground (400m), upstairs (176 steps), and downstairs (176 steps), and a subset of 10 subjects performed treadmill walking trials to assess the influence of walking speed on accuracy. Activity monitor measured step count and distance values were compared with actual step count (determined from video recordings) and distance to determine accuracy. RESULTS: For level walking, step count errors in Yamax CW-701, Fitbit Zip, Fitbit One, Omron HJ-321, and Jawbone UP 24 were within 1% and distance errors in Fitbit Zip and Yamax CW-701 were within 5%. Garmin Vivofit and Omron HJ-321 were the most accurate in estimating step count for stairs with errors less than 4%. An important finding is that all activity monitors overestimated distance for stair walking by at least 45%. CONCLUSION: In general, there were not accuracy differences among activity monitors for stair walking. Accuracy did not change between moderate and fast walking speeds, though slow walking increased errors for some activity monitors. Nike+ FuelBand was the least accurate step count estimator during all walking tasks. Caution should be taken when interpreting step count and distance estimates for activities involving stairs.

A microfabricated nanocalorimeter: design, characterization, and chemical calibration.

A microfabricated titration calorimeter having nanowatt sensitivity is presented. The device is achieved by modifying a commercial, suspended-membrane, thin-film thermopile infrared sensor. Chemical reactions are studied by placing a 50.0 nL droplet of one reagent directly on the sensor and injecting nanoliter droplets of a second reagent through a micropipette by means of a pressure-driven droplet injector with 1% reliability in volume delivery. External thermal noise is minimized by a two-layer thermal shielding system. Evaporation is prevented by positioning the micropipette through a tiny hole in a cover glass, sealed by a drop of oil. The device is calibrated using two acid-base reactions: H2SO4 + HEPES buffer, and NaOH + HCl. The measured power sensitivity is 2.90(4) V/W, giving a detection limit of 22 nW. The 1/e time constant for a single injection is 1.1 s. The day-to-day power sensitivity is reproducible to approximately 2%. A computational model of the sensor reproduces the power sensitivity within 10% and the time constant within 20%. For a 50 nL sample and 0.8-1.5 nL titrant injection volumes, the heat uncertainty of 44 nJ corresponds to a 3sigma detection limit of 132 nJ, or the binding energy associated with 2.9 pM of IgG-protein A complex.

High-resolution high-speed panoramic cardiac imaging system.

A panoramic cardiac imaging system consisting of three high-speed CCD cameras has been developed to image the surface electrophysiology of a rabbit heart via fluorescence imaging using a voltage-sensitive fluorescent dye. A robust, unique mechanical system was designed to accommodate the three cameras and to adapt to the requirements of future experiments. A unified computer interface was created for this application - a single workstation controls all three CCD cameras, illumination, stimulation, and a stepping motor that rotates the heart. The geometric reconstruction algorithms were adapted from a previous cardiac imaging system. We demonstrate the system by imaging a polymorphic cardiac tachycardia.

A phased-array stimulator system for studying planar and curved cardiac activation wavefronts.

Wavefront propagation in cardiac tissue is affected greatly by the geometry of the wavefront. We describe a computer-controlled stimulator system that creates reproducible wavefronts of a predetermined shape and orientation for the investigation of the effects of wavefront geometry. We conducted demonstration experiments on isolated perfused rabbit hearts, which were stained with the voltage-sensitive dye, di-4-ANEPPS. The wavefronts were imaged using a laser and a charge-coupled device (CCD) camera. The stimulator and imaging systems have been used to characterize the relationship between wavefront velocity and fiber orientation. This approach has potential applications in investigating curvature effects, testing numerical models of cardiac tissue, and creating complex wavefronts using one-, two-, or three-dimensional electrode arrays.