Sikder, DebabrataRafin, Nafiz ImtiazAlam, Md. Golam RabiulUddin M.Z.2026-09-292026-09-292024-01-01D. Sikder, N. I. Rafin, M. G. R. Alam and M. Z. Uddin, "Smartphone Sensor-based Human Activity Recognition Using LSTM Networks: Development and Implementation in Android Applications," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 203-208, doi: 10.1109/ICCIT64611.2024.11021865.97983315190942-s2.0-105009075911https://hdl.handle.net/10361/30294The machine learning approach to estimate human activity using smartphone sensor data is challenging. In this work, a Human Activity Recognition (HAR) approach is conducted based on the Long Short-Term Memory (LSTM) model, which can recognize six different behaviors: Downstairs, Jogging, Sitting, Standing, Upstairs, and Walking. To achieve the best potential result, various machine learning and statistical approaches were explored. The LSTM model, known for its effectiveness in sequence prediction, was chosen for its ability to run efficiently on lightweight edge devices such as smartphones. This model achieved a test accuracy of 97%. Finally, the model was exported and deployed in an Android application, providing a user-friendly interface.6 Pagesen-USLegged locomotionDeep learningRecurrent neural networksAccuracyComputational modelingPredictive modelsBehavioral sciencesHuman activity recognitionInformation technologyLong short term memoryMachine Learning (ML)Deep learningHuman activity (HA)Human activity recognition (HAR)TensorFlowRecurrent neural networks (RNNs)Long short-term memory networks (LSTM)Human activity recognition.Human behavior.Conference Proceeding10.1109/ICCIT64611.2024.11021865