SenStick: Comprehensive Sensing Platform with an Ultra Tiny All-In-One Sensor Board for IoT Research

We propose a comprehensive sensing platform called SenStick, which is composed of hardware (ultra tiny all-in-one sensor board), software (iOS, Android, and PC), and 3D case data. The platform aims to allow all the researchers to start IoT research, such as activity recognition and context estimation, easily and efficiently. The most important contribution is the hardware that we have designed. Various sensors often used for research are embedded in an ultra tiny board with the size of 50 mm ( ) × 10 mm ( ) × 5 mm ( ) and weight around 3 g including a battery. Concretely, the following sensors are embedded on this board: acceleration, gyro, magnetic, light, UV, temperature, humidity, and pressure. In addition, this board has BLE (Bluetooth low energy) connectivity and capability of a rechargeable battery. By using 110 mAh battery, it can run more than 15 hours. The most different point from other similar boards is that our board has a large flash memory for logging all the data without a smartphone. By using SenStick, all the users can collect various data easily and focus on IoT data analytics. In this paper, we introduce SenStick platform and some case studies. Through the user study, we confirmed the usefulness of our proposed platform.

[1]  Yutaka Arakawa,et al.  SenStick 2: ultra tiny all-in-one sensor with wireless charging , 2016, UbiComp Adjunct.

[2]  Yutaka Arakawa.  SenStick: sensorize every things , 2015, UbiComp/ISWC Adjunct.

[3]  Paul Lukowicz,et al.  Recognizing the Use-Mode of Kitchen Appliances from Their Current Consumption , 2009, EuroSSC.

[4]  Ning Liu,et al.  Bathroom Activity Monitoring Based on Sound , 2005, Pervasive.

[5]  Yutaka Arakawa,et al.  WaistonBelt: a belt for monitoring your real abdominal circumference forever , 2015, UbiComp/ISWC Adjunct.

[6]  Jee-In Kim,et al.  Smart Belt : A wearable device for managing abdominal obesity , 2016, 2016 International Conference on Big Data and Smart Computing (BigComp).

[7]  Yutaka Arakawa,et al.  Indoor localization based on distance-illuminance model and active control of lighting devices , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[8]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[9]  Paul M. Matthews,et al.  The UK Biobank. , 2015, Brain : a journal of neurology.

[10]  Oliver Brdiczka,et al.  Detecting Human Behavior Models From Multimodal Observation in a Smart Home , 2009, IEEE Transactions on Automation Science and Engineering.

[11]  Kazuya Murao,et al.  Personal identification system based on rotation of toilet paper rolls , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[12]  Jacob C. Seidell,et al.  The Global Burden of Obesity and the Challenges of Prevention , 2015, Annals of Nutrition and Metabolism.

[13]  Muhammad Usman Ilyas,et al.  Activity recognition using smartphone sensors , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[14]  Miwako Doi,et al.  Indoor-outdoor activity recognition by a smartphone , 2012, UbiComp.