DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation


DeepASL technology enables realtime two-way communcation between deaf population and normal hearings. In effect, deaf people simply perform sign language as usual and DeepASL will capture the hand shape and movement information, transfrom into recognized word or sentence, and "speak" with synthesized voice. To recognize them, we use a hierarhical bidirectional RNN appended with Connectionist Temporal Classification (CTC) losss.

Paper in ACM SenSys 2017. Data collection tool and dataset releasing soon.

Multi-Task Learning Age-Gender Identification and DCGANS on Face Regeneration and Completion


In this project, we realize age and gender identification using multi-task learning in CNN. We also generate new faces using generative adversarial network (GAN) boosted by age-gender multi-task learning. We show that multi-task learning achieves better performance than single-task learning. We also demonstrate that GAN achieves high performance in generating faces.

Technical Report

BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring


BodyScan is able to detect more than ten daily activities and monitor breath rate using a smartwatch and hip-mounted device. It transmits WiFi signal, receives the signal that bounces off user's body, and classifies the signal into recognized activities using SVM. Breath rate is detected using power spectral density.

Paper in ACM MobiSys 2016.

HeadScan: A Wearable System for Radio-based Sensing of Head and Mouth-related Activities


HeadScan enables contactless sensing of head and mouth related activities. Simply with two antennas on both shoulders, HeadScan is able to recognize whether you eat, cough, drink and speak. HeadScan potentially benefits the medical treatment and care of diabetes and autism population.

Paper in ACM/IEEE IPSN 2016.

Media: Stanford Medicine, MedGadget, Futurity, MSUToday, Fox 2, ReadWrite WLNS6

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AirSense: An Intelligent Home-based Sensing System for Indoor Air Quality Analytics


AirSense technology detects and classifies the pollution events (e.g., smoke and cook) that happen indoor. It also visualizes the indoor air quality (IAQ) in your smartphone application. We use PM2.5, volatile organic compound (VOC) and humidity sensors to differentiate pollution events. AirSense could increase the awareness of indoor pollution of people.

Paper in ACM Ubicomp 2016.

Media: Futurity, MSUToday, ASHARE, DBusiness



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