Free Sensing

Free sensing uses ambient wireless signals or scattered light to infer information about the environment or objects without dedicated sensors, aiming for efficient and privacy-preserving data acquisition. Current research focuses on developing robust algorithms, often employing convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to extract meaningful information from these signals, with applications ranging from human activity recognition to object classification. This approach holds significant promise for applications requiring low-power, low-cost, and privacy-respecting sensing solutions in areas like smart homes, healthcare monitoring, and security systems.