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Exploring the Real-Time Variability and Complexity of Sitting Patterns in Office Workers with Non-Specific Chronic Spinal Pain and Pain-Free Individuals
Publication . Oliosi, Eduarda; Júlio, Afonso; Probst, Phillip; Silva, Luís M.; Vilas-Boas, João Paulo; Pinheiro, Ana Rita; Gamboa, Hugo; LIBPhys-UNL; MDPI - Multidisciplinary Digital Publishing Institute
Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing the same task multiple times, different performance patterns can be observed. This variability is intrinsic to all biological systems and is noticeable in human movement. This study aims to examine whether changes in movement variability and complexity during real-time office work are influenced by CSP. The hypothesis is that individuals with and without pain will have different responses to office work tasks. Six office workers without pain and ten with CSP participated in this study. Participant’s trunk movements were recorded during work for an entire week. Linear and nonlinear measures of trunk kinematic displacement were used to assess movement variability and complexity. A mixed ANOVA was utilized to compare changes in movement variability and complexity between the two groups. The effects indicate that pain-free participants showed more complex and less predictable trunk movements with a lower degree of structure and variability when compared to the participants suffering from CSP. The differences were particularly noticeable in fine movements.
Cleaning ECG with Deep Learning
Publication . Dias, Mariana; Probst, Phillip; Silva, Luís M.; Gamboa, Hugo; LIBPhys-UNL; Springer
As the popularity of wearables continues to scale, a substantial portion of the population has now access to (self-)monitorization of cardiovascular activity. In particular, the use of ECG wearables is growing in the realm of occupational health assessment, but one common issue that is encountered is the presence of noise which hinders the reliability of the acquired data. In this work, we propose an ECG denoiser based on bidirectional Gated Recurrent Units (biGRU). This model was trained on noisy ECG samples that were created by adding noise from the MIT-BIH Noise Stress Test database to ECG samples from the PTB-XL database. The model was initially trained and tested on data corrupted with the three most common sources of noise: electrode motion artifacts, muscle activation and baseline wander. After training, the model was able to fully reconstruct previously unseen signals, achieving Root-Mean-Square Error values between 0.041 and 0.023. For further testing the model’s robustness, we performed a data collection in an industrial work setting and employed our model to clean the noisy data, acquired from 43 workers using wearable sensors. The trained network proved to be very effective in removing real ECG noise, outperforming the available open-source solutions, while having a much smaller complexity compared to state-of-the-art Deep Learning approaches.
Cardiorespiratory Response to Workload Volume and Ergonomic Risk
Publication . Furk, Dania; Silva, Luís; Dias, Mariana; Fujão, Carlos; Probst, Phillip; Liu, Hui; Gamboa, Hugo; DF – Departamento de Física; LIBPhys-UNL; MDPI - Multidisciplinary Digital Publishing Institute
Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.
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Fundação para a Ciência e a Tecnologia
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PRT/BD/152843/2021