TCS gesture recognition using ultrasound sensor:
US11960654B2 Acoustic system and method based gesture detection using spiking neural networks 20220409
Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).
[0079] … This converted SNN can be implemented on neuromorphic platforms such as Brainchip Akida (e.g., refer “Brainchip unveils the akidatm development environment,” https://www.brainchipinc.com/news-m...chip-unveils-the-akida-developmentenvironment, 2019”), Intel® Loihi (e.g., refer “Mike Davies. et. al, “Advancing neuromorphic computing with loihi: A survey of results and outlook,” Proceedings of the IEEE, vol. 109, no. 5, pp. 911-934, 2021.”), etc. to achieve further power benefit (˜100×).
US11960654B2 Acoustic system and method based gesture detection using spiking neural networks 20220409
Inventors
GIGIE ANDREW [IN]; GEORGE ARUN [IN]; KUMAR ACHANNA ANIL [IN]; DEY SOUNAK [IN]; PAL ARPAN [IN]Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).
[0079] … This converted SNN can be implemented on neuromorphic platforms such as Brainchip Akida (e.g., refer “Brainchip unveils the akidatm development environment,” https://www.brainchipinc.com/news-m...chip-unveils-the-akida-developmentenvironment, 2019”), Intel® Loihi (e.g., refer “Mike Davies. et. al, “Advancing neuromorphic computing with loihi: A survey of results and outlook,” Proceedings of the IEEE, vol. 109, no. 5, pp. 911-934, 2021.”), etc. to achieve further power benefit (˜100×).
