Guohao Lan

Transportation mode detection using kinetic energy harvesting wearables

Detecting the transportation mode of an individuals everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, accelerometer and GPS are the dominantly used signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we investigate the feasibility of using the output voltage from the kinetic energy harvesting device as the signal source to achieve transportation mode detection. The proposed idea is based on the intuition that the vibrations experienced by the passenger during motoring of different transportation modes are different. Thus, voltage generated by the energy harvesting devices should contain distinctive features to distinguish different transportation modes. Using the dataset collected from a real energy harvesting device, we present the initial demonstration of the proposed method. We can achieve 98.84% of accuracy in determining whether the user is traveling by pedestrian or motorized modes, and in a fine-grained classification of three different motorized modes (car, bus, and train), an overall accuracy over 85% is achieved.

Estimating calorie expenditure from output voltage of piezoelectric energy harvester: an experimental feasibility study

There is a growing interest in developing energy harvesting solutions for wearable devices so they can self-power themselves without relying on batteries. Piezoelectric energy harvesters (PEHs) can convert kinetic energy released from human activities into usable electrical energy for powering various electronic circuits inside the wearable device. Intuitively, the kinetic energy is produced because the user expends some calories during the physical activities. We therefore postulate that the voltage output of a PEH in a wearable device should contain information that can be used to estimate the amount of calorie expended. If this is true, then the PEH can be used as a new source for calorie estimation. Unlike conventional sensors, such as accelerometers, a PEH does not consume any power, which would make this new source very attractive. In this paper, using real PEH hardware and the data collected from ten real subjects, we conduct an experimental study to assess the suitability of PEH voltage in estimating calorie expenditure for two different activities, walking and running. We find that, for most subjects, the calorie estimations obtained from the output voltage of PEH is very close to those obtained from a 3-axial accelerometer.

HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.

A Bayesian framework for energy-neutral activity monitoring with self-powered wearable sensors

Achieving energy-efficiency is a challenging task in human activity monitoring. The continuous activity sensing using accelerometer and the burdensome on-node classification rapidly deplete the limited battery resource of the wearable nodes. To reduce the energy overhead and achieve the system energy-neutrality, we present a novel Bayesian framework for human activity monitoring using the energy-harvesting wearable sensors. The proposed framework utilizes a capacitor to store the harvested kinetic energy and uses all the stored energy to transmit an unmodulated signal, called an activity pulse. Our framework can infer the human activity directly from the received signal strength of the activity pulse at a remote server. Neither accelerometer nor classifier is required on the wearable devices, and therefore, our framework guarantees the system energy-neutrality. Using a real dataset collected from a kinetic energy harvester coupled with a Bluetooth prototype, an overall accuracy of 91% is achieved when the distance between the transmitter and the receiver is set to 30 cm.

Kryptein: a compressive-sensing-based encryption scheme for the internet of things

Internet ofThings (IoT) is flourishing and has penetrated deeply into people’s daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud.The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption.e performance of Kryptein is also measured on off -the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.

VEH-COM: Demodulating vibration energy harvesting for short range communication

This paper investigates the possibility of using a vibration energy harvesting (VEH) device as a communication receiver. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, we aim to transmit small amounts of data at low rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming digital signal processing (DSP), which makes a VEH receiver significantly more power efficient than a conventional microphone-based decoder. To address the extremely narrow bandwidth of VEH, we design a simple ON-OFF keying modulation, but optimized for VEH hardware. Experiments with a real VEH device shows that, at a distance of 2 cm, a laptop speaker with the proposed modulation scheme can achieve 30 bps communication for a target bit error rate of less than 1%, which would enable many emerging short range applications, such as mobile payment. The communication range of a laptop can be extended to 80 cm for 5 bps, allowing a range of other audio-based device-to-device communications, such as a web advertisement on a laptop browser transferring tokens to a nearby smartphone. We also demonstrate that the proposed VEH-based sound decoding is resilient to background noise, thanks to its extremely narrow power harvesting bandwidth, which works as a natural noise filter.

Gesture Recognition with Transparent Solar Cells: A Feasibility Study

Transparent solar cell is an emerging solar energy harvesting technology that allows us to see through these cells. This revolutionary discovery is creating unique opportunities to turn any mobile device screen into solar energy harvester. In this paper, we consider the possibility of using such energy harvesting screens as a sensor to detect hand gestures. As different gestures impact the incident light on the screen in a different way, they are expected to create unique energy generation patterns for the transparent solar cell. Our goal is to recognize gestures by detecting these solar energy patterns. A key uncertainty we face with transparent solar cell is that, to provide transparency, they cannot harvest from the visible spectra, which may lead to weaker energy patterns for the gestures. To study gesture recognition feasibility of transparent solar cell, we develop a 1cmx1cm organic see-through solar cell which provides high level of content visibility when placed on mobile phone screen. We then use the output current of the organic cell as the source signal for gesture pattern recognition using machine learning. Experimental results demonstrate that we can detect five hand gestures with average accuracies of 95%. We also compare gesture recognition accuracies of our prototype organic cell with those obtained from a conventional ceramic opaque solar cell, which reveals that organic solar cell can recognize some of these gestures almost as good as the opaque cells.

Energy-efficient acoustic communication using vibration energy harvesting

With the ubiquity of microphone-enabled pervasive device, the use of speaker-microphone to transfer small piece of information has become a hot area in both industry and research communities. Unfortunately, however, microphone-based acoustic communication systems rely on power-consuming digital signal processing (DSP) to decode the modulated information in the sound. Given the battery lifetime of todays mobile devices is limited, microphone-based systems are facing challenges in achieving long-term computing and communication. In this proposal, we aim to investigates the possibility of using a vibration energy harvesting (VEH) device as an receiver for energy-efficient acoustic communication. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, our current system prototype [1] is able to transmit small amounts of data at reasonable rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming DSP, which makes a VEH receiver more power efficient than a conventional microphone-based decoder. As part of our future work, we will further improve and optimize the performance of our prototype system while ensure better user experience and system security.

Unobtrusive User Verification using Piezoelectric Energy Harvesting

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve self-powered wearable system. Apart from generating energy to power the wearable devices, the output electricity signal of the PEH can also be used as an information source as it reflects the activity or motion patterns of the user. In this paper, we have designed and built an insole-based user authentication system by leveraging the AC voltage generated by the PEH during human walking. Meanwhile, the generated power is also collected and stored, which could be later used as the power source of the mobile system. By using a dataset of 20 subjects, we have demonstrated that our system can achieve 89.76% of human recognition accuracy when using only one gait cycle signal, and the accuracy can be further increased to 95.86% when two gait cycles are utilized.

CapSense: Capacitor-based Activity Sensing for Kinetic Energy Harvesting Powered Wearable Devices

We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises significant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 different activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, after considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.