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Since construction workers often need to carry various types of loads in their daily routine, they are at risk of sustaining musculoskeletal injuries. Additionally, carrying a load during walking may disturb their walking balance and lead to fall injuries among construction workers. Different load carrying techniques may also cause different extents of physical exertion. Therefore, the purpose of this paper is to examine the effects of different load-carrying techniques on gait parameters, dynamic balance, and physiological parameters in asymptomatic individuals on both stable and unstable surfaces.

Fifteen asymptomatic male participants (mean age: 31.5 ± 2.6 years) walked along an 8-m walkway on flat and foam surfaces with and without a load thrice using three different techniques (e.g. load carriage on the head, on the dominant shoulder, and in both hands). Temporal gait parameters (e.g. gait speed, cadence, and double support time), gait symmetry (e.g. step time, stance time, and swing time symmetry), and dynamic balance parameters [e.g. anteroposterior and mediolateral center of pressure (CoP) displacement, and CoP velocity] were evaluated. Additionally, the heart rate (HR) and electrodermal activity (EDA) was assessed to estimate physiological parameters.

The gait speed was significantly higher when the load was carried in both hands compared to other techniques (Hand load, 1.02 ms vs Head load, 0.82 ms vs Shoulder load, 0.78 ms). Stride frequency was significantly decreased during load carrying on the head than the load in both hands (46.5 vs 51.7 strides/m). Step, stance, and swing time symmetry were significantly poorer during load carrying on the shoulder than the load in both hands (Step time symmetry ration, 1.10 vs 1.04; Stance time symmetry ratio, 1.11 vs 1.05; Swing time symmetry ratio, 1.11 vs 1.04). The anteroposterior (Shoulder load, 17.47 mm vs Head load, 21.10 mm vs Hand load, −5.10 mm) and mediolateral CoP displacements (Shoulder load, −0.57 mm vs Head load, −1.53 mm vs Hand load, −3.37 ms) significantly increased during load carrying on the shoulder or head compared to a load in both hands. The HR (Head load, 85.2 beats/m vs Shoulder load, 77.5 beats/m vs No load, 69.5 beats/m) and EDA (Hand load, 14.0 µS vs Head load, 14.3 µS vs Shoulder load, 14.1 µS vs No load, 9.0 µS) were significantly larger during load carrying than no load.

The findings suggest that carrying loads in both hands yields better gait symmetry and dynamic balance than carrying loads on the dominant shoulder or head. Construction managers/instructors should recommend construction workers to carry loads in both hands to improve their gait symmetry and dynamic balance and to lower their risk of falls.

The potential changes in gait and balance parameters during various load carrying methods will aid the assessment of fall risk in construction workers during loaded walking. Wearable insole sensors that monitor gait and balance in real-time would enable safety managers to identify workers who are at risk of falling during load carriage due to various reasons (e.g. physical exertion, improper carrying techniques, fatigue). Such technology can also empower them to take the necessary steps to prevent falls.

This is the first study to use wearable insole sensors and a photoplethysmography device to assess the impacts of various load carrying approaches on gait parameters, dynamic balance, and physiological measures (i.e. HR and EDA) while walking on stable and unstable terrains.

Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.

A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.

For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.

Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

The purpose of this paper is to understand what dimensions consumers prefer to track using wearable technology to achieve a healthier lifestyle and how these tracking dimensions are related.

An online survey was conducted with potential consumers in the USA, and a series of Pearson’s correlation and regression analysis and multiple regressions was conducted.

The most preferred self-tracking dimensions, tracking dimensions on others, most private tracking dimensions, most variable dimensions, and the dimensions that need to be improved were identified. The results of this study showed positive relationships overall among similar types of tracking dimensions, such as among dimensions of physical health condition (disease and disorder symptoms and general vital signs), mental health condition (stress level and mood/feeling), healthy lifestyle (fitness, and pose and posture), and productivity and task management (work productivity, location, and time management).

Designers are encouraged to make wearable technology products that are durable, easy to care for, attractive in design, comfortable to wear and use, able to track preferred dimensions, appropriate for various consumers, unobtrusive, portable, and small. This research will guide wearable technology and fashion industry professionals in the development process of wearable technology to benefit consumers by helping them be more self-aware, empowering them to develop a healthier lifestyle, and ultimately increasing their quality of life and well-being.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

To assess the ability of generative AI to assist in crisis management planning and response.

The viewpoint is built on a “conversation” with ChatGPT (CGPT) on the subject of crisis management. As such, portions of the text were generated by CGPT, a Large Language Model (LLM) and not by the author.

While CGPT has mastered the language of crisis management, its ability to assist in real-life situations is probably limited. Paradoxically, it believes it can help provide predictive analytics even though it claims not to be able to assess future events.

The author believes that the paradoxes inherent in CGPT’s claims to be able to assist in crisis management have not previously been examined.

This study aims to explore the effect of different volumes of indoor greenery on perceived stress, stress, perceived productivity, productivity and perceived workplace satisfaction to support employees’ well-being.

In a cross-over experimental design, a homogeneous group of students was randomised in different orders where each participant experienced the same three conditions. Different volumes of indoor greenery were added to the experiment room (0%, 0.5% and 8%). With a Fitbit, sudoku and a questionnaire, the five variables were measured.

Findings show that perceived stress and heart rate (physical stress) are lower in the 8% condition. Productivity scores did not improve significantly, however perceived productivity did in the 8% condition. Positive trends could be seen for the variables in the 0.5% condition. Furthermore, 2/3 of the participants preferred the 8% condition, whereas the other 1/3 preferred the 0.5% condition. Overall, 1/3 of the participants mentioned that their optimal volume of greenery would be more than 0.5% but less than 8%. This research shows that the implementation of 8% indoor greenery contributes to a happier and more relaxing office place where employees get the feeling of being less stressed and more productive.

This research found that the largest volume of plants used (8%) has the most positive effect on the variables studied in this research. This study shows that participants’ heart rate, and therefore physical stress, became lower in the 8% condition. In this 8% condition, participants experienced less stress and perceived themselves to be more productive. Besides, 2/3 of the participants preferred being in the 8% condition, suggesting that after adding this volume of indoor greenery employees will be more satisfied with their workplace.

Where previous research discovered that indoor greenery has a positive effect on variables like (perceived) stress, (perceived) productivity and perceived workplace satisfaction, this research also provides support for the effect of different volumes of indoor greenery. Results obtained by the spatial coverage ratio approach can be easily applied to future research and practice.

Indoor greenery can contribute to making the office a happier and more relaxing place where employees get the feeling of being less stressed and more productive which in the long term might contribute to the overall well-being of employees.

To the best of the authors’ knowledge, this is the first study focusing on the effect of different volumes of indoor greenery on these five variables.

The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN).

Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting.

Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances.

The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment.

This is the first time respiratory patterns have been applied in biometric prediction system design.

This paper presents a critical review of studies which map the urban environment using continuous physiological data collection. A conceptual model is consequently presented for mitigating urban stress at the city and the user level.

The study reviews relevant publications, examining the tools used for data collection and the methods used for data analysis and data fusion. The relationship between urban features and physiological responses is also examined.

The review showed that the continuous monitoring of physiological data in the urban environment can be used for location-aware stress detection and urban emotion mapping. The combination of physiological and contextual data helps researchers understand how the urban environment affects the human body. The review indicated a relationship between some urban features (green, land use, traffic, isovist parameters) and physiological responses, though more research is needed to solidify the existence of the identified links. The review also identified many theoretical, methodological and practical issues which hinder further research in this area.

While there is large potential in this field, there has been no review of studies which map continuously physiological data in the urban environment. This study covers this gap and introduces a novel conceptual model for mitigating urban stress.