Dastagir Muhammad Bilal Akram, Tariq Omer, Han Dongsoo
Korea Advanced Institute of Science and Technology-KAIST, Daejeon 34141, Republic of Korea.
Sensors (Basel). 2024 Aug 7;24(16):5125. doi: 10.3390/s24165125.
Accurate indoor-outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98-99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor-outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.
精确的室内外检测（IOD）对于基于位置的服务、情境感知计算和移动应用至关重要，因为它能提高服务的相关性和精度。然而，传统的IOD方法仅依赖GPS数据，由于信号受阻，在室内环境中常常失效；而在实时应用中，由于泛化性降低，惯性测量单元（IMU）数据对于未见数据不可靠。本研究通过引入DeepIOD框架来填补这一研究空白，该框架利用IMU传感器数据、GPS和光线信息来准确地将环境分类为室内或室外。该框架对输入数据进行预处理，并采用多个深度神经网络模型，使用自适应多数投票机制组合输出，以确保稳健可靠的预测。使用智能手机在六个未见环境上进行评估的实验结果表明，DeepIOD比仅使用IMU传感器的方法具有显著更高的准确率。我们的DeepIOD系统实现了98 - 99%的卓越准确率，转换时间小于10毫秒。本研究得出结论，DeepIOD为室内外分类提供了一个具有高泛化性的稳健可靠解决方案，凸显了整合多种数据源以改善基于位置的服务和其他需要精确环境情境感知的应用的重要性。
