Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month.

Wearable wristbands have become prevailing in the recent days because of their small and portable property. However, the limited size of the touch screen causes the problems of fat fingers and screen occlusion. Furthermore, it is not available for users whose hands are fully occupied with other tasks. To break this bottleneck, we propose a portable, hand-free and secure text-entry system, called FaceInput, which firstly uses a single small form factor sensor to accomplish a practical user input via facial vibrations. To sense the tiny facial vibration signals, we design and implement a double-stage amplifier whose maximum gain is 225. To enhance the input accuracy and robustness, we design a set of novel schemes for FaceInput based on the Mel-frequency cepstral coefficient (MFCC) concept and a hidden Markov model (HMM) to process the vibration signals, and an online calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects during the period of one month. The results demonstrate that FaceInput can be successful to sense the tiny facial vibrations and robust to fight against various confounding factors. The average recognition accuracy is 98.2%. Furthermore, by enabling the runtime calibration and adaptation scheme that updates and enlarges the training data set, the accuracy can reach 100%.

Nowadays, smart wristbands have become one of the most prevailing wearable devices as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to extend the input. Yet by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which firstly leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rate, ViType designs a set of novel mechanisms, including an artificial neural network to process the vibration signals, and a runtime calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust to fight against various confounding factors. The average recognition accuracy is 94.8% with an initial training sample size of 20 for each key, which is 1.52 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98% on average during one month.

Localization in complex indoor environments with RF signal is a challenging task. Via this technique, the signal is easily affected by obstacles and environmental noise due to the broadcast nature of RF signal transmission. In this paper, we observe that seismic signal is more oriented than the RF signal, and thus is more suitable for localization in an complex indoor environment with obstacles. Motivated by this observation, we propose a device-free passive indoor localization system, namely Floc, that can fight against environmental impact and achieve high localization accuracy. FLoc is composed of three modules, which are sensing module that collects seismic signal from footsteps, footstep detection module that remove the environmental impact and recover the clean footsteps, and localization module that leverages seismic signal from footsteps for positioning. We implement FLoc on credit-card sized single-board computer Raspberry Pi equipped with geophones. To verify the effectiveness of our system, we conduct extensive experiments for different scenario in a complex indoor environment with the area of 6 × 8 square meter. The experimental results demonstrates that Floc can achieve up to 7cm localiztion accuracy on average, and can outperform the existing acoustic signal-based localization techniques.