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We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual’s wellbeing. Since sound is the most-reported environmental factor causing stress and discomfor...
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... using the classical approach are trained using the R packages lavaan 19 and nlme 20 in a 16 GB RAM, 2.7 GHz processor PC, whereas the empirical Bayes model was written and executed using Stan program through the RStan interface 17 , in a highperformance computer cluster with 28 nodes (192 GB RAM per node, Intel Haswell v3 28 core processors). The predictions from the models for SDNN and normalized-HF are compared with the (actual) measured values of the two measures to compute Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) 21 (Table 2). Table 2 shows that the model trained using the empirical Bayes model has the lowest RMSE and MAPE, indicating that our method is superior to other methods for simultaneous modeling of SDNN and normalized-HF. ...Context 2
... predictions from the models for SDNN and normalized-HF are compared with the (actual) measured values of the two measures to compute Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) 21 (Table 2). Table 2 shows that the model trained using the empirical Bayes model has the lowest RMSE and MAPE, indicating that our method is superior to other methods for simultaneous modeling of SDNN and normalized-HF. ...Similar publications
001 Abstract Purpose: The conventional long cane, largely used for mobility of visually impaired people, presents some limitations, such as the short-range detection of obstacles and the inability to protect against head-level obstacles. The purpose of this study was to develop and test a wearable haptic interface system attachable to a long cane i...
In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has...
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... Furthermore, humidity outside the 30%-60% relative humidity (RH) range -less than 30% RH or greater than 60% RH -was associated with a 25% higher stress response (Razjouyan et al., 2020). Finally, noise levels less than 35 or 40 decibels as well as noise levels greater than 45 decibels were associated with higher stress levels (Srinivasan et al., 2023). Personality also plays a role, with persons scoring high on extraversion being happier in open office settings and more focused than those scoring high on neuroticism (introverts) (Baranski et al., 2023) In combination, these findings point to the need for many choices in office design, with a variety of types of spaces for people to gather in different sized groups for different purposes, with options for quiet heads down spaces when needed. ...
... One of the main applications of fNIRS is the measure of brain cortex hemodynamics in response to neural activity, which allows researchers to investigate a wide range of cognitive and motor processes connected to rehabilitation. It has also been widely leveraged in clinics to assess cerebral hemodynamics in patients suffering from stroke [94], TBI [95], or dementia [96]. Recent technological improvements allowed also its exploitation in delicate environments such as neonatal intensive care units (ICUs), where fNIRS is particularly suitable for assessing the cerebral autoregulation of preterm infants [97]. ...
In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients’ state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients’ state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.
Indoor environmental quality (IEQ) significantly impacts human health, well-being, and productivity. However, a comprehensive and in-depth review of the combined effects of IAQ and other multi-domain factors on human productivity is lacking. There has not been any prior review that encapsulates the impact of multi-domain factors on productivity and physiological responses of occupants. To address this gap, this review paper investigates and highlights the impact of IAQ and multi-domain factors (thermal, visual, and acoustic) on human productivity and occupant well-being in the built environment. The review explores various research methods, including evaluation of human productivity and creativity, data collection, and physiological signal analysis. We also examined the interactions between IAQ and multi-domain factors, as well as strategies for optimizing productivity through integrated building design and smart systems. The key findings from this review reveal that IAQ significantly impacts human productivity and occupant well-being, with interactions between IAQ and other IEQ factors further impacting these effects. Despite advances in the field, there are several limitations and gaps in the current research methods and study designs, including small sample sizes, limited and insufficient experimental design and control, reliance on laboratory or simulated environments, lack of follow-up and long-term data, and lack of robust performance metrics. The review proposes future research directions, including specific applications, and follow-up work to address these limitations and further advance the understanding of IAQ and multi-domain factors in the built environment. The implications of this review for policy and practice include the need for holistic and integrated approaches to IAQ and IEQ management, with a focus on creating healthy and productive indoor environments. This review emphasizes the importance of considering the complex interplay between IAQ and multi-domain factors, as well as the potentials of adopting smart control systems and sustainable design strategies to optimize productivity and occupant well-being in the built environment. By addressing these critical issues, we can enhance the overall quality of life for building occupants and contribute to a more sustainable future.
Health and wellness/wellbeing are multifaceted topics further complicated when trying to understand environmental impact. Typically, there has been a one size fits all approach when trying to understand the 3-way interaction but that is a limited approach. Equally, measurement (of each) has often used a limited set of outcomes during short periods to provide insight. A more robust understanding of health and wellbeing within environments may require longitudinal/continuous assessment that holistically targets individuals. Therefore, there is a growing requirement for careful data management, individual-first methodologies, scalable research designs and new analytical approaches e.g., artificial intelligence. That presents many challenges but interesting research opportunities for the field of digital medicine.
