{"ID":2839919,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02025","arxiv_id":"2512.02025","title":"DySTAN: Joint Modeling of Sedentary Activity and Social Context from Smartphone Sensors","abstract":"Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role in understanding user behavior, yet is often overlooked in mobile sensing research. To address these gaps, we introduce LogMe, a mobile sensing application that passively collects smartphone sensor data (accelerometer, gyroscope, magnetometer, and rotation vector) and prompts users for hourly self-reports capturing both sedentary activity and social context. Using this dual-label dataset, we propose DySTAN (Dynamic Cross-Stitch with Task Attention Network), a multi-task learning framework that jointly classifies both context dimensions from shared sensor inputs. It integrates task-specific layers with cross-task attention to model subtle distinctions effectively. DySTAN improves sedentary activity macro F1 scores by 21.8% over a single-task CNN-BiLSTM-GRU (CBG) model and by 8.2% over the strongest multi-task baseline, Sluice Network (SN). These results demonstrate the importance of modeling multiple, co-occurring context dimensions to improve the accuracy and robustness of mobile context recognition.","short_abstract":"Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role in understanding user...","url_abs":"https://arxiv.org/abs/2512.02025","url_pdf":"https://arxiv.org/pdf/2512.02025v1","authors":"[\"Aditya Sneh\",\"Nilesh Kumar Sahu\",\"Snehil Gupta\",\"Haroon R. Lone\"]","published":"2025-11-18T08:28:54Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\",\"cs.HC\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}