Influence of passive arm-support exoskeleton on static postural balance in load-holding tasks: effects of supportive force, weight and load location.

This study systematically investigated the influence of a passive arm-support exoskeleton (ASE) on static postural balance in load-holding tasks under different weight conditions and load locations, and the relationship between such influence and the exoskeleton's supportive force profile. Using a 3 (exoskeleton interventions) ×3 (weight conditions) ×3 (load locations) within-subjects design, the research found that wearing ASE with supportive force significantly reduced postural sway by 17.84% and 15.19% across all conditions compared to without wearing the exoskeleton and with deactivated support, respectively. These improvements were consistent with subjective assessments. The stability benefits varied with the weight and load location, reflecting the exoskeleton's supportive force profile. Overall, the study suggests that the supportive force from the ASE can enhance static postural balance, with effectiveness dependent on weight conditions and load locations. This highlights the potential of passive ASEs for improving stability and reducing fall risks in work environments.

A multifactorial fall risk assessment system for older people utilizing a low-cost, markerless Microsoft Kinect.

Falls among older people are a major health concern. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. A Kinect-based test battery was designed to comprehensively assess major fall risk factors. A follow-up experiment was conducted with 102 older participants to assess their fall risks. Participants were divided into high and low fall risk groups based on their prospective falls over a 6-month period. Results showed that the high fall risk group performed significantly worse on the Kinect-based test battery. The developed random forest classification model achieved an average classification accuracy of 84.7%. In addition, the individual's performance was computed as the percentile value of a normative database to visualise deficiencies and targets for intervention. These findings indicate that the developed system can not only screen out 'at risk' older individuals with good accuracy, but also identify potential fall risk factors for effective fall intervention.Practitioner summary: Falls are the leading cause of injuries in older people. We newly developed a multifactorial fall risk assessment system for older people utilising a low-cost, markerless Kinect. Results showed that the developed system can screen out 'at risk' individuals and identify potential risk factors for effective fall intervention.

Acute Effects of Carbon Fiber Insole on Three Aspects of Sports Performance, Lower Extremity Muscle Activity, and Subjective Comfort.

Carbon fiber insole (CFI), which is lightweight and stiff to reduce energy loss and help wearers perform better in sports, has recently been introduced. However, reports are scarce on the effects of CFI on sports performance, muscle activation, and wearing comfort. This study investigated the acute effects of CFI on sports performance, lower extremity muscle activity, and subjective comfort. Thirty young healthy males with shoe sizes between 260 and 270 mm performed various sports tasks (power generation, agility, and speed) and treadmill runs with wearable sensors under two experimental insole conditions (benchmark insole as a baseline, CFI). The results showed that, compared to the benchmark insole, CFI significantly improved sports performance in terms of power generation (~1.5%) and agility (~1%). However, it activated more of the Tibialis Anterior (~0.7%) and Gastrocnemius Medialis (~0.8%) muscles, and was perceived to be stiffer and less comfortable. These findings suggested that CFI could improve sports performance, but could cause more lower extremity muscle activation and subjective discomfort.

TinyFallNet: A Lightweight Pre-Impact Fall Detection Model.

Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% < 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB > 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.

Transcriptomic, proteomic, and physiological studies reveal key players in wheat nitrogen use efficiency under both high and low nitrogen supply.

The effective use of available nitrogen (N) to improve crop grain yields provides an important strategy to reduce environmental N pollution and promote sustainable agriculture. However, little is known about the common genetic basis of N use efficiency (NUE) at varying N availability. Two wheat (Triticum aestivum L.) cultivars were grown in the field with high, moderate, and low N supply. Cultivar Zhoumai 27 outperformed Aikang 58 independent of the N supply and showed improved growth, canopy leaf area index, flag leaf surface area, grain number, and yield, and enhanced NUE due to both higher N uptake and utilization efficiency. Further, transcriptome and proteome analyses were performed using flag leaves that provide assimilates for grain growth. The results showed that many genes or proteins that are up- or down-regulated under all N regimes are associated with N and carbon metabolism and transport. This was reinforced by cultivar differences in photosynthesis, assimilate phloem transport, and grain protein/starch yield. Overall, our study establishes that improving NUE at both high and low N supply requires distinct adjustments in leaf metabolism and assimilate partitioning. Identified key genes/proteins may individually or concurrently regulate NUE and are promising targets for maximizing crop NUE irrespective of the N supply.

Nitrogen Regulating the Expression and Localization of Four Glutamine Synthetase Isoforms in Wheat (Triticum aestivum L.).

Glutamine synthetase (GS), the key enzyme in plant nitrogen assimilation, is strictly regulated at multiple levels, but the most relevant reports focus on the mRNA level. Using specific antibodies as probes, the effects of nitrogen on the expression and localization of individual wheat GS (TaGS) isoforms were studied. In addition to TaGS2, TaGS1;1 with high affinity to substrate and TaGS1;3 with high catalytic activity were also localized in mesophyll, and may participate in cytoplasmic assimilation of ammonium (NH4+) released from photorespiration or absorbed by roots; TaGS1;2 was localized in xylem of leaves. In roots, although there were hundreds of times more TaGS1;1 than TaGS1;2 transcripts, the amount of TaGS1;1 subunit was not higher than that of TaGS1;2; NH4+ inhibited TaGS1;1 expression but stimulated TaGS1;3 expression. In root tips, nitrate stimulated TaGS1;1, TaGS1;3, and TaGS2 expression in meristem, while NH4+ promoted tissue differentiation and TaGS1;2 expression in endodermis and vascular tissue. Only TaGS1;2 was located in vascular tissue of leaves and roots, and was activated by glutamine, suggesting a role in nitrogen transport. TaGS1;3 was induced by NH4+ in root endodermis and mesophyll, suggesting a function in relieving NH4+ toxicity. Thus, TaGS isoforms play distinct roles in nitrogen assimilation for their different kinetic properties, tissue locations, and response to nitrogen regimes.

Effects of working posture, lifting load, and standing surface on postural instability during simulated lifting tasks in construction.

Postural instability is a major contributor to fatal and nonfatal falls in the construction industry. This study investigated the effects of working posture, lifting load and standing surface on perceived postural instability. Thirty young males performed simulated lifting tasks in construction using six different postures under four experimental conditions (2 loads × 2 surfaces). Results showed working postures with bending at the waist and overhead carrying were associated with high postural instability. With lifting load and inclined standing surface both significantly increased postural instability for all working postures except the full squatting. Full squatting with lifting load was more stable than without load for the flat surface, but opposite for the inclined surface. These findings indicate three investigated factors had not only significant main effects, but also complicated interaction effects on postural instability, implying that all three factors should be considered simultaneously for the real practice on fall prevention in construction. Practitioner summary: The leading causes of worker deaths in the construction industry were falls. This study showed that working postures with waist bending and overhead carrying were associated with high postural instability. With lifting load and inclined standing surface both significantly increased postural instability for all working postures except the full squatting.

A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People.

Older people face difficulty engaging in conventional rehabilitation exercises for improving physical functions over a long time period due to the passive nature of the conventional exercise, inconvenience, and cost. This study aims to develop and validate a dynamic time warping (DTW) based algorithm for assessing Kinect-enabled home-based physical rehabilitation exercises, in order to support auto-coaching in a virtual gaming environment. A DTW-based algorithm was first applied to compute motion similarity between two time series from an individual user and a virtual coach. We chose eight bone vectors of the human skeleton and body orientation as the input features and proposed a simple but innovative method to further convert the DTW distance to a meaningful performance score in terms of the percentage (0-100%), without training data and experience of experts. The effectiveness of the proposed algorithm was validated through a follow-up experiment with 21 subjects when playing a Tai Chi exergame. Results showed that the algorithm scores had a strong positive linear relationship (r = 0.86) with experts' ratings and the calibrated algorithm scores were comparable to the gold standard. These findings suggested that the DTW-based algorithm could be effectively used for automatic performance evaluation of an individual when performing home-based rehabilitation exercises.

A Unified Deep-Learning Model for Classifying the Cross-Country Skiing Techniques Using Wearable Gyroscope Sensors.

The automatic classification of cross-country (XC) skiing techniques using data from wearable sensors has the potential to provide insights for optimizing the performance of professional skiers. In this paper, we propose a unified deep learning model for classifying eight techniques used in classical and skating styles XC-skiing and optimize this model for the number of gyroscope sensors by analyzing the results for five different configurations of sensors. We collected data of four professional skiers on outdoor flat and natural courses. The model is first trained over the flat course data of two skiers and tested over the flat and natural course data of a third skier in a leave-one-out fashion, resulting in a mean accuracy of ~80% over three combinations. Secondly, the model is trained over the flat course data of three skiers and tested over flat course and natural course data of one new skier, resulting in a mean accuracy of 87.2% and 95.1% respectively, using the optimal sensor configuration (five gyroscope sensors: both hands, both feet, and the pelvis). High classification accuracy obtained using both approaches indicates that this deep learning model has the potential to be deployed for real-time classification of skiing techniques by professional skiers and coaches.

Accuracy of Base of Support Using an Inertial Sensor Based Motion Capture System.

The potential of miniature inertial sensors for human balance and gait analysis appears promising. Base of support (BOS), together with its interaction with center of mass, is a critical indicator in above mentioned research fields. This study aims to evaluate the accuracy of Xsens MVN BIOMECH, a commercial widely used inertial sensor-based motion capture system, for measuring static BOS and examine the effect of different task complexity on the accuracy. Eleven young males participated in this study and went through eleven different experimental tasks. Results showed there were considerable errors in estimating BOS area (error ranged from -12.6% to +64.6%) from Xsens MVN and a large error in foot separation distance when there was knee flexion. The estimated BOS area from MVN was smaller than the ground truth from footprint when there was no knee flexion, and larger when there was knee flexion, and it increased monotonically along with the knee flexion angles. Wrongly estimated foot separations, mainly caused by knee flexion, and the initial system estimation error on BOS, were two major reasons for error and instability of BOS estimation. The findings suggested that caution should be taken when using Xsens MVN BIOMECH to estimate BOS and foot position-related measurements, especially for postures/motions with knee flexion.

Comparison of seven fall risk assessment tools in community-dwelling Korean older women.

This study aimed to compare seven widely used fall risk assessment tools in terms of validity and practicality, and to provide a guideline for choosing appropriate fall risk assessment tools for elderly Koreans. Sixty community-dwelling Korean older women (30 fallers and 30 matched non-fallers) were evaluated. Performance measures of all tools were compared between the faller and non-faller groups through two sample t-tests. Receiver Operating Characteristic curves were generated with odds ratios for discriminant analysis. Results showed that four tools had significant discriminative power, and the shortened version of Falls Efficacy Scale (SFES) showed excellent discriminant validity, followed by Berg Balance Scale (BBS) with acceptable discriminant validity. The Mini Balance Evaluation System Test and Timed Up and Go, however, had limited discriminant validities. In terms of practicality, SFES was also excellent. These findings suggest that SFES is the most suitable tool for assessing the fall risks of community-dwelling Korean older women, followed by BBS. Practitioner Summary: There is no general guideline on which fall risk assessment tools are suitable for community-dwelling Korean older women. This study compared seven widely used assessment tools in terms of validity and practicality. Results suggested that the short Falls Efficacy Scale is the most suitable tool, followed by Berg Balance Scale.

Effects of high heeled shoes wearing experience and heel height on human standing balance and functional mobility.

This study aimed to examine the effects of high heeled shoes (HHS) wearing experience and heel height on human standing balance and functional mobility. Thirty young and healthy females (ten experienced and twenty inexperienced HHS wearers) participated in a series of balance tests when they wore shoes of four different heel heights: 1 cm (flat), 4 cm (low), 7 cm (medium) and 10 cm (high). Experimental results show that regardless of the wearing experience, the heel elevation induces more effort from lower limb muscles (particularly calf muscles) and results in worse functional mobility starting at 7 cm heel height. While the heel height increased to 10 cm, the standing balance also becomes worse. Experienced HHS wearers do not show significantly better overall performance on standing balance and functional mobility than inexperienced controls, even though they have better directional control (76.8% vs. 74.4%) and larger maximum excursion (93.3% vs. 89.7%). To maintain standing balance, experienced wearers exert less effort on tibialis anterior, vastus lateralis and erector spinae muscles at the cost of more intensive effort from gastrocnemius medialis muscle. PRACTITIONER SUMMARY: Many women wear high heeled shoes (HHS) to increase female attractiveness. This study shows that HHS induce more muscular effort and worse human standing balance and functional mobility, especially when heel height reaches 10 cm. HHS wearing experience only provides certain advantages to wearers on limits of stability in terms of larger maximum excursion and better directional control.

Load distribution to minimise pressure-related pain on foot: a model.

The optimal force distribution to minimise pain or discomfort at the foot-shoe interface is still not known. Most shoe-related products attempt to distribute the load uniformly without much consideration to the bony and soft tissue regions. An experiment was conducted to first determine the pressure pain threshold (PPT) and tissue deformation on the plantar surface of the foot. Circular probes of areas 0.5, 1.0 and 2.0 cm(2) at indentation speeds of 0.5, 1 and 2 mm/s showed that PPT depends on the location stimulated, area of stimulation and the indentation speed. The results also showed that tissue stiffness is quite low for small deformations ( < 4 mm), but significantly higher at large deformations (>4 mm). The stiffness at the larger deformation region was positively correlated with PPT (r = 0.63, p < 0.001). The data were further used to develop a model with PPT, deformation and stimulated area. PRACTITIONER SUMMARY: Pressure at which there is an onset of pain is higher when a larger area of soft tissue is stimulated. Bony areas may be better suited to bear load on smaller areas to minimise pressure-related pain. Thus, manipulating supporting surface stiffness and surface contours can help minimise pain.

Trunk range of motion: A wearable sensor-based test protocol and indicator of fall risk in older people.

Conventionally, trunk range of motion (TROM) requires manual measurement by an external health professional with a general-purpose goniometer. This study aims to propose a convenient test protocol to assess TROM based on a single wearable sensor and to further investigate the relationship between TROM and fall risk of older people. We first explored the optimal sensor position by comparing TROMs from four representative locations (T1, T12, L5 and sternum) and optical motion capture system (golden reference). A follow-up experiment was conducted to evaluate the relationship between TROM and fall risk. The results showed that T12 achieved the minimum root mean square error (3.8 ± 2.2°) against the golden reference and the non-faller group had significantly higher TROMs than the faller group. These findings suggest that the newly proposed protocol is convenient yet valid and TROM can be a promising indicator of fall risk in older people.

Data Augmentation to Address Various Rotation Errors of Wearable Sensors for Robust Pre-impact Fall Detection.

OBJECTIVE: This paper proposes a novel application of data augmentation to address various rotation errors of wearable sensors for robust pre-impact fall detection. In such systems, sensor rotation errors are inevitable because of loose attachment and body movement during long deployment. METHODS: Two augmented models with uniform and normal strategies were compared with a non-augmented model on the original dataset (no rotation error) and a validation dataset (with rotation error). The validation dataset was constructed with three types of rotation errors, namely, pitch, roll, and compound roll and pitch (CRP) at three levels of range (low: 15°, medium: 30°, and high: 45°). RESULTS: Five-fold cross validation showed the two augmented models maintained accuracy (>98.5%) as high as the non-augmented model on the original dataset but showed considerable improvements of 6.11% and 6.50% on the validation dataset, respectively. CRP error negatively affected the model accuracy the most, followed by pitch and then roll errors. In addition, the normal model had advantages over the uniform model in the low-to-medium range of error, which is expected to be the typical error range in practical applications. As for lead time, similarly, the augmented models achieved performance similar to the non-augmented model on the original dataset but showed significant improvements on the validation dataset. CONCLUSION AND SIGNIFICANCE: Data augmentation had notable capacities to address sensor rotation errors for practical applications and augmented models especially the normal model showed good potential to be embedded in a wearable system for robust pre-impact fall detection and injury prevention.

Modelling for design and evaluation of industrial exoskeletons: A systematic review.

Industrial exoskeletons are developed to relieve workers' physical demands in the workplace and to alleviate ergonomic issues associated with work-related musculoskeletal disorders. As a safe and economical alternative to empirical/experimental methods, modelling is considered as a powerful tool for design and evaluation of industrial exoskeletons. This systematic review aims to provide a comprehensive understanding of the current literature on the design and evaluation of industrial exoskeletons through modelling. A systematic study was conducted by general keyword searches of five electronic databases over the last two decades (2003-2022). Out of the 701 records initially retrieved, 33 eligible articles were included and analyzed in the final review, presenting a variety of model inputs, model development, and model outputs used in the modelling. This systematic review study revealed that existing modelling methods can evaluate the biomechanical and physiological effects of industrial exoskeletons and provide some design parameters. However, the modelling method is currently unable to cover some of the main evaluation metrics supported by experimental assessments, such as task performance, user experience/discomfort, change in metabolic costs etc. Standard guidelines for model construction and implementation, as well as validation of human-exoskeleton interactions, remain to be established.

Adaptability of wheat to future climate change: Effects of sowing date and sowing rate on wheat yield in three wheat production regions in the North China Plain.

Adjusting sowing dates and sowing rates is a key adaptation strategy for adapting to future climate change, and maintaining wheat production in the North China Plain (NCP). However, it is still unclear whether the current sowing date and sowing rate can adapt to future climate change, and how to adjust the sowing date and sowing rate to compensate for the adverse effects of climate change on wheat yields. This study predicts the adaptability of agricultural management practices like sowing dates and sowing rates, to future climate change in three wheat regions by referring to four global climate models (GCMs) and AquaCrop model. Population structure and yield were maximized for sowing dates from Oct.11-20 and sowing rates of 10-13 kg/667 m2 (or 13-16 kg/667 m2) in 2016-2021. Three wheat regions were expected to show a warming trend, while the total precipitation has large spatial fluctuations under both representative concentration pathways (rcp) scenarios in the 2022-2100. AquaCrop model could simulate yield with a good precision (RMSE≤1043.7 kg/ha). Compared to the average yield of the baseline period (2016-2021), in the 2022-2100, the average predicted wheat yields of three wheat regions simulated based on the current optimal sowing date and sowing rate decreased by 5.45 % âˆ¼ 11.05 % (9.35 % âˆ¼ 16.84 %) and 2.57 % âˆ¼ 10.95 % (6.97 % âˆ¼ 12.75 %) under the rcp4.5 (rcp8.5), respectively. Average wheat yield losses were effectively compensated when the combinations of Oct.15 and 14 kg/667 m2 for the dryland wheat, Oct.21 and 14 kg/667 m2 for the irrigated wheat, and Oct.21 and 13 kg/667 m2 for the high-yield-rainfed wheat were applied under both rcp scenarios, respectively, with predicted yield losses of -4.17 %, -3.50 %, and - 3.25 %. Thus, adjusting sowing dates and sowing rates are viable options to effectively address the adverse effects of future global climate change, thereby guaranteeing food security in the NCP.

Effects of elevated atmospheric [CO2] on grain starch characteristics in different specialized wheat.

The increasing atmospheric [CO2] poses great challenges to wheat production. Currently, the response of starch characteristics in different specialized wheat cultivars to elevated [CO2], as well as the underlying physiological and molecular mechanisms remains unclear. Therefore, an experiment was conducted with open-top chambers to study the effects of ambient [CO2] [a(CO2)] and elevated [CO2] [e(CO2)] on photosynthetic performance, yield and starch characteristics of bread wheat (Zhengmai 369, ZM369) and biscuit wheat (Yangmai 15, YM15) from 2020 to 2022. The results demonstrated a significant improvement in photosynthetic performance, yield, amylose and amylopectin content, volume ratio of large granules under e[CO2]. Moreover, e[CO2] upregulated the gene expression and enzyme activities of GBSS (Granule-bound starch synthase) and SSS (Soluble starch synthase), increased starch pasting viscosity, gelatinization enthalpy and crystallinity. Compared to YM15, ZM369 exhibited a higher upregulation of GBSSI, greater increase in amylose content and volume ratio of large granules, as well as higher gelatinization enthalpy and crystallinity. However, ZM369 showed a lower increase in amylopectin content and a lower upregulation of SSSI and SSSII. Correlation analysis revealed amylose and amylopectin content had a positive correlation with GBSS and SSS, respectively, a significant positively correlation among the amylose and amylopectin content, starch granule volume, and pasting properties. In conclusion, these changes may enhance the utilization value of biscuit wheat but exhibit an opposite effect on bread wheat. The results provide a basis for selecting suitable wheat cultivars and ensuring food security under future climate change conditions.

ROS-Triggered Self-Assembled Nanoparticles Based on a Chemo-Sonodynamic Combinational Therapy Strategy for the Noninvasive Elimination of Hypoxic Tumors.

The hypopermeability and hypoxia in the tumor milieu are important factors that limit multiple treatments. Herein, the reactive oxygen species (ROS)-triggered self-assembled nanoparticles (RP-NPs) was constructed. The natural small molecule Rhein (Rh) was encapsulated into RP-NPs as a sonosensitizer highly accumulated at the tumor site. Then highly tissue-permeable ultrasound (US) irradiation induced apoptosis of tumor cells through the excitation of Rh and acoustic cavitation, which prompted the rapid production of large amounts of ROS in the hypoxic tumor microenvironment. In addition, the thioketal bond structures in the innovatively designed prodrug LA-GEM were triggered and broken by ROS to achieve rapid targeted release of the gemcitabine (GEM). Sonodynamic therapy (SDT) increased the tissue permeability of solid tumors and actively disrupted redox homeostasis via mitochondrial pathways to kill hypoxic tumor cells, and the triggered response mechanism to GEM synergistically amplified the effect of chemotherapy. The chemo-sonodynamic combinational treatment approach is highly effective and noninvasive, with promising applications for hypoxic tumor elimination, such as in cervical cancer (CCa) patients who want to maintain their reproductive function.

ZmGI2 regulates flowering time through multiple flower development pathways in maize.

GIGANTEA (GI) encodes a component of the circadian clock core oscillator and has been identified as a regulatory pathway of the circadian rhythm and photoperiodic flowering in model plants. However, the regulatory pathway of GI affecting flowering time is unknown in maize. Here, we identified that the zmgi2 mutant flowered earlier than the wild type under long day (LD) conditions, whereas the difference in flowering time was not apparent under short day (SD) conditions. The 24 h optimal expression of the gene in the stem apex meristems (SAM) appeared at 9 h after dawn under LD conditions and at 11 h after dawn under SD conditions. DAP-Seq and RNA-Seq further revealed that ZmGI2 delays flowering by directly binding to the upstream regions of ZmVOZs, ZmZCN8 and ZmFPF1 to repress the expression of these genes and by directly binding to the upstream regions of ZmARR11, ZmDOF and ZmUBC11 to promote the expression of these genes. The genetic and biochemical evidence suggests a model for the potential role of ZmGI2 in regulating the flowering time-dependent photoperiodic pathway. This study provides novel insights into the function of ZmGIs in maize and further demonstrates their potential importance for floral transition. These results contribute to a comprehensive understanding of the molecular mechanisms and regulatory networks of GI transcription factors in regulating flowering time in maize.