Body Posture Position Alarm Prototype Based on NodeMCU ESP8266

Lack of physical activity has a negative impact, namely reduced motor coordination abilities and changes in posture or shape of the spine. Sitting positions that are more static and less ergonomic, such as sitting in a hunched position, can trigger significant muscle activation. Therefore, in an effort to prevent bone abnormalities, research was carried out regarding a prototype body posture alarm based on the NodeMCU ESP8266. This prototype uses a flexible sensor to read spinal curvature integrated into the NodeMCU ESP8266 and a buzzer as the output. This prototype will be attached to the back support shoulder, so this prototype design can also help repair bones that have been damaged due to bad sitting habits. In general, this prototype reminds users to always be in a normal body position by making a sound when the body position is not normal. From the test results, the prototype works well. NodeMCu's speed in capturing WiFi signals is fast enough so that the prototype works quickly, flexible sensor readings are accurate without using an amplifier. The back support shoulder design is very efficient in helping users to maintain a normal body position

Combining inertial-based ergonomic assessment with biofeedback for posture correction: a narrative review

Work-related musculoskeletal disorders (WRMSDs) are the most reported work-related health problem in the European Union, representing an economic burden equivalent to 2% of its gross domestic product. Awkward postures are one of the main risk factors. Several postural assessment tools try to identify ergonomic exposure factors for evaluating WRMSD risk, yet these are commonly based on observation. Replacing observations with objective measurements can bring more accuracy and reproducibility to this analysis; hence, a direct measurement approach for the assessment is desired. This review looks for two-fold solutions, able to not only monitor workers’ posture using inertial sensors but also to return that information to the user, in a biofeedback loop. It presents systems for posture risk assessment, regarding ergonomic methods, sensors’ and actuators’ characteristics, and validation protocols. In particular, this review advances previous manuscripts by exploring the literature regarding different biofeedback strategies and ways to encode meaningful information in the cues, i.e., able to deliver intuitive ergonomic guidance so that the user becomes aware and changes into a more neutral posture. The combination of inertial sensors and vibrotactile motors stood out, due to its effectiveness in reducing postural risk. Directional feedback to guide users’ segments individually was found to be a promising strategy, although its validation is still limited. The results of the reviewed manuscripts pointed out the relevant practices, potentialities, and limitations of the existing solutions, allowing the identification of future challenges.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and the INTEGRATOR project under Grant 2022.15668.MIT. Sara Cerqueira was supported by the doctoral Grant FRH/BD/151382/2021, financed by FCT, under MIT Portugal Program

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace

Promoting a healthy ageing workforce: use of Inertial Measurement Units to monitor potentially harmful trunk posture under actual working conditions

Musculoskeletal disorders, particularly those involving the low back, represent a major health concern for workers, and originate significant consequences for the socio-economic system. As the average age of the population is gradually (yet steadily) increasing, such phenomenon directly reflects on labor market raising the need to create the optimal conditions for jobs which must be sustainable for the entire working life of an individual, while constantly ensuring good health and quality of life. In this context, prevention and management of low back disorders (LBDs) should be effective starting from the working environment. To this purpose, quantitative, reliable and accurate tools are needed to assess the main parameters associated to the biomechanical risk. In the last decade, the technology of wearable devices has made available several options that have been proven suitable to monitor the physical engagement of individuals while they perform manual or office working tasks. In particular, the use of miniaturized Inertial Measurement Units (IMUs) which has been already tested for ergonomic applications with encouraging results, could strongly facilitate the data collection process, being less time- and resources-consuming with respect to direct or video observations of the working tasks. Based on these considerations, this research intends to propose a simplified measurement setup based on the use of a single IMUs to assess trunk flexion exposure, during actual shifts, in occupations characterized by significant biomechanical risk. Here, it will be demonstrated that such approach is feasible to monitor large groups of workers at the same time and for a representative duration which can be extended, in principle, to the entire work shift without perceivable discomfort for the worker or alterations of the performed task. Obtained data, which is easy to interpret, can be effectively employed to provide feedback to workers thus improving their working techniques from the point of view of safety. They can also be useful to ergonomists or production engineers regarding potential risks associated with specific tasks, thus supporting decisions or allowing a better planning of actions needed to improve the interaction of the individual with the working environment

Wearable technology for posture monitoring at the workplace

Prolonged strenuous postures in occupational context may lead to low back pain. Avoiding such occurrences is known to help prevent low back pain episodes or may contribute to recovery. This research concerns wearable sensing technology to support posture monitoring for the prevention of occupational low back pain and, more specifically, how smart garments can help nurses avoid prolonged strenuous postures at work. We introduce BackUp, a system comprising of a smart shirt connected to a smartphone application that provides feedback and advice on low back posture, and we describe its design and implementation. We report on a series of studies that contributed to its development: an anthropometric study (N = 60) to decide on the placement of sensors on the lower spine; a brief field study aimed at evaluating user experience and attitudes towards the shirt (N = 17), and a second field study intended to assess its effectiveness in helping nurses avoid prolonged strenuous postures at work (N = 13). These studies demonstrate how smart clothing can support posture feedback in real life conditions. While the results from the field studies are encouraging regarding the potential of this technology, further research is needed to establish the durability of the behaviour modification achieved through smart garments

Wearable technology for posture monitoring at the workplace

\u3cp\u3eProlonged strenuous postures in occupational context may lead to low back pain. Avoiding such occurrences is known to help prevent low back pain episodes or may contribute to recovery. This research concerns wearable sensing technology to support posture monitoring for the prevention of occupational low back pain and, more specifically, how smart garments can help nurses avoid prolonged strenuous postures at work. We introduce BackUp, a system comprising of a smart shirt connected to a smartphone application that provides feedback and advice on low back posture, and we describe its design and implementation. We report on a series of studies that contributed to its development: an anthropometric study (N = 60) to decide on the placement of sensors on the lower spine; a brief field study aimed at evaluating user experience and attitudes towards the shirt (N = 17), and a second field study intended to assess its effectiveness in helping nurses avoid prolonged strenuous postures at work (N = 13). These studies demonstrate how smart clothing can support posture feedback in real life conditions. While the results from the field studies are encouraging regarding the potential of this technology, further research is needed to establish the durability of the behaviour modification achieved through smart garments.\u3c/p\u3