Remote photoplethysmography (rPPG) enables noncontact heart rate (HR) estimation using facial videos and exhibits significant convenience over traditional contact-based HR measurement approaches. However, its effectiveness depends mainly on the number of pixels of the facial region; thus, the estimation accuracy is easily degraded due to missing rPPG information when the resolution of facial images is low. Given that, in most real application scenarios, such as group-oriented and long-distance vital signs measurement, only low-resolution facial videos are available, finding a solution to recover rPPG information is a key to improve the performance of noncontact HR measurement. In this article, we propose a two-stage deep learning scheme to achieve accurate HR measurement from low-resolution facial videos. In the first stage, an rPPG information recovery network is proposed to recover the rPPG information during video super-resolution (SR) processing. The up-scaled images are then fed into a temporally aware HR measurement network in the second stage. An attention mechanism that reassigns the weights of the temporal information in each feature channel is designed to improve the measurement accuracy. The mean absolute percentage error achieves 3.320 and 4.231, while Pearson's correlation coefficient reaches 0.930 and 0.892 on two datasets, respectively. Experimental results show that rPPG information could be effectively recovered through SR processing. Compared with the state-of-the-art approaches that treat high-resolution images as the input, the proposed method achieved closely comparable measurement performance using merely low-resolution images.

Treadmill exercise is commonly used as an alternative to over-ground walking or running. Increasing evidence indicated the kinetics of treadmill exercise is different from that of over-ground. Biomechanics of treadmill or over-ground exercises have been investigated in terms of energy consumption, ground reaction force, and surface EMG signals. These indexes cannot accurately characterize the musculoskeletal loading, which directly contributes to tissue injuries. This study aimed to quantify the differences of lower limb joint angles and muscle forces in treadmills and over-ground exercises. 10 healthy volunteers were required to walk at 100 and 120 steps/min and run at 140 and 160 steps/min on treadmill and ground. The joint flexion angles were obtained from the motion capture experiments and were used to calculate the muscle forces with an inverse dynamic method. Hip, knee, and ankle joint motions of treadmill and over-ground conditions were similar in walking, yet different in running. Compared with over-ground running, joint motion ranges in treadmill running were smaller. They were also less affected by stride frequency. Maximum Gastrocnemius force was greater in treadmill walking, yet maximum Rectus femoris and Vastus forces were smaller. Maximum Gastrocnemius and Soleus forces were greater in treadmill running. Treadmill exercise results in smoother joint kinematics. In terms of muscle force, treadmill exercise requires lower loading on knee extensor, yet higher loading on plantar flexor, especially on Gastrocnemius. The findings and the methodology can provide the basis for rehabilitation therapy customization and sophistic treadmill design.

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

Long exposure to the microgravity will lead to muscle atrophy and bone loss. Treadmill exercise could mitigate the musculoskeletal decline. But muscle atrophy remains inevitable. The constraint loading applied on astronauts could affect the muscle force and its atrophy severity. However, the quantitative correlation between constraint loading mode and muscle forces remains unclear. This study aimed to characterize the influence of constraint loading mode on the lower limb muscle forces in weightless treadmill exercise. The muscle forces in the full gait cycle were calculated with the inverse dynamic model of human musculoskeletal system. The calculated muscle forces at gravity were validated with the EMG data. Muscle forces increased at weightlessness compared with those at the earth’s gravity. The increasing percentage from high to low is as follows: biceps femoris, gastrocnemius, soleus, vastus, and rectus femoris, which was in agreement with the muscle atrophy observed in astronauts. The constraint loading mode had an impact on the muscle forces in treadmill exercise and thus could be manipulated to enhance the effect of the muscle training in spaceflight. The findings could provide biomechanical basis for the optimization of treadmill constraint system and training program and improve the countermeasure efficiency in spaceflight.

Objective: Prolonged microgravity environments impair skeletal muscle homeostasis by triggering fiber-type transitions and metabolic dysregulation. Although exercise and nutritional interventions may alleviate disuse atrophy, their synergistic effects under microgravity conditions remain poorly characterized. This study investigated the effects of an 8-week ketogenic diet combined with aerobic exercise in hindlimb-unloaded mice on muscle fiber remodeling and metabolic adaptation. Methods: Seven-week-old male C57BL/6J mice were randomly divided into six groups: normal diet control (NC), normal diet with hindlimb unloading (NH), normal diet with hindlimb unloading and exercise (NHE), ketogenic diet control (KC), ketogenic diet with hindlimb unloading (KH), and ketogenic diet with hindlimb unloading and exercise (KHE). During the last two weeks of intervention, hindlimb unloading was applied to simulate microgravity. Aerobic exercise groups performed moderate-intensity treadmill running (12 m/min, 60 min/day, and 6 days/week) for 8 weeks. Body weight, blood ketone, and glucose levels were measured weekly. Post-intervention assessments included the respiratory exchange ratio (RER), exhaustive exercise performance tests, and biochemical analyses of blood metabolic parameters. The skeletal muscle fiber-type composition was evaluated via immunofluorescence staining, lipid deposition was assessed using Oil Red O staining, glycogen content was analyzed by Periodic Acid–Schiff (PAS) staining, and gene expression was quantified using quantitative real-time PCR (RT-qPCR). Results: Hindlimb unloading significantly decreased body weight, induced muscle atrophy, and reduced exercise endurance in mice. However, the combination of KD and aerobic exercise significantly attenuated these adverse effects, as evidenced by increased proportions of oxidative muscle fibers (MyHC-I) and decreased proportions of glycolytic fibers (MyHC-IIb). Additionally, this combined intervention upregulated the expression of lipid metabolism-associated genes, including CPT-1b, HADH, PGC-1α, and FGF21, enhancing lipid metabolism and ketone utilization. These metabolic adaptations corresponded with improved exercise performance, demonstrated by the increased time to exhaustion in the KHE group compared to other hindlimb unloading groups. Conclusions: The combination of a ketogenic diet and aerobic exercise effectively ameliorates simulated microgravity-induced skeletal muscle atrophy and endurance impairment, primarily by promoting a fiber-type transition from MyHC-IIb to MyHC-I and enhancing lipid metabolism gene expression (CPT-1b, HADH, and PGC-1α). These findings underscore the potential therapeutic value of combined dietary and exercise interventions for mitigating muscle atrophy under simulated microgravity conditions.

With the advancement of crewed spaceflight, mitigating the physiological effects of microgravity, such as bone–muscle deterioration and movement instability, has become increasingly vital. Inspired by reptilian climbing mechanisms, this study presents a novel bio-inspired adhesive footwear characterized by low pre-load, strong adhesion, and controllable attachment–detachment capability. This study analyzes the adaptability of a multi-level variable modulus design to surfaces with varying curvatures and roughness. Experimental investigations were conducted to analyze the contact mechanics and interfacial mechanisms of biomimetic adhesive materials featuring microstructure arrays. Moreover, stepping exercises were performed by volunteers wearing the proposed footwear under simulated weightlessness to assess biomechanical performance. Interface contact stresses were measured using force-sensing array plates, enabling characterization of plantar adhesion under different detachment speeds and angles. Electromyographic signals from lower limb muscle groups during stepping exercises were analyzed to elucidate the mechanical stimulation patterns and effects induced by plantar adhesion forces. Results indicate that plantar adhesion forces ranging between 50 and 105 N effectively stimulate primary flexor muscles, including the biceps femoris and gastrocnemius. This biomimetic solution offers a flexible and convenient approach for stabilizing foot positioning and promoting musculoskeletal engagement in microgravity, improving astronauts’ mobility and operational performance in orbit.