Browsing by Author "Xu M"

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    Lung cancer risk in painters: results from the SYNERGY pooled case-control study consortium
    (BMJ Publishing Group Ltd, 2021-04) Guha N; Bouaoun L; Kromhout H; Vermeulen R; Brüning T; Behrens T; Peters S; Luzon V; Siemiatycki J; Xu M; Kendzia B; Guenel P; Luce D; Karrasch S; Wichmann H-E; Consonni D; Landi MT; Caporaso NE; Gustavsson P; Plato N; Merletti F; Mirabelli D; Richiardi L; Jöckel K-H; Ahrens W; Pohlabeln H; TSE LA; Yu IT-S; Tardón A; Boffetta P; Zaridze D; 't Mannetje A; Pearce N; Davies MPA; Lissowska J; Świątkowska B; McLaughlin J; Demers PA; Bencko V; Foretova L; Janout V; Pándics T; Fabianova E; Mates D; Forastiere F; Bueno-de-Mesquita B; Schüz J; Straif K; Olsson A
    OBJECTIVES: We evaluated the risk of lung cancer associated with ever working as a painter, duration of employment and type of painter by histological subtype as well as joint effects with smoking, within the SYNERGY project. METHODS: Data were pooled from 16 participating case-control studies conducted internationally. Detailed individual occupational and smoking histories were available for 19 369 lung cancer cases (684 ever employed as painters) and 23 674 age-matched and sex-matched controls (532 painters). Multivariable unconditional logistic regression models were adjusted for age, sex, centre, cigarette pack-years, time-since-smoking cessation and lifetime work in other jobs that entailed exposure to lung carcinogens. RESULTS: Ever having worked as a painter was associated with an increased risk of lung cancer in men (OR 1.30; 95% CI 1.13 to 1.50). The association was strongest for construction and repair painters and the risk was elevated for all histological subtypes, although more evident for small cell and squamous cell lung cancer than for adenocarcinoma and large cell carcinoma. There was evidence of interaction on the additive scale between smoking and employment as a painter (relative excess risk due to interaction >0). CONCLUSIONS: Our results by type/industry of painter may aid future identification of causative agents or exposure scenarios to develop evidence-based practices for reducing harmful exposures in painters.
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    Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning
    (MDPI AG, 24/03/2023) Noble F; Xu M; Alam F
    Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.