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KEH-Sense: Technology for self-powering wearables

KEH-Sense: Technology for self-powering wearables


Recent Advancements in energy harvesting hardware have created an opportunity for realizing self-powered wearables for continuous and pervasive human context recognition. Unfortunately, the power requirements of the continuous sensing using different sensors, such as accelerometers, and the burdensome on-node classification and communication are relatively high compared to the amount of power that can be practically harvested, which limit the energy harvesting’s usefulness.


A generic architecture of self-powered wearable devices


This research introduces a novel approach that employs kinetic energy harvesting (KEH) and infers human context information directly from the KEH patterns without using any other sensors such as accelerometers which need continuous power to operate. The underlying idea lies in the fact that different ambient vibrations generate energy in a different way producing different energy generation patterns in the KEH circuit. Because no actual sensor such as accelerometer is needed, a significant percentage of the limited harvestable energy can be saved, making significant progress towards self-powered autonomous wearables.



Conventional accelerometer-based vibration recognition in self-powered device        



 Proposed architecture of KEH-Sense


In this project, we quantify the capabilities of KEH-Sense for various applications:

  • On-going investigations
    • Human Activity Recognition
    • Step Counting
    • Calorie Expenditure Estimation
    • User Authentication
    • Transportation Mode Detection
    • Railway location tracking
    • Voice-activated Hotword Detection (eg. “Hey Siri”)
    • Short Range Audio Communication Receiver


All of our results are based on real data collected under natural (non-laboratory) conditions. Using KEH Recorder I (presented below), we collected extensive real data to demonstrate KEH as a potential new source of information for the previously mentioned  applications. The table below summaries the data collection details, the algorithms used, and the accuracy reported in each study, confirming KEH as an efficient source of information for a wide range of wearable applications.


Application Data Collection Algorithm Used Accuracy
Human Activity Recognition 10 subjects, 5 different activities, 2 holding positions K-nearest neighbour (KNN) 81% for hand and 87% for waist
Step Counting 4 subjects, different walking scenarios Peak detection algorithm 96%
Calorie Expenditure Estimation 10 subject, 2 different activites Standard statistical regression 88% for walking and 84% for running
Hotword Detection 8 subjects Decision tree classifier 73% for speaker independent and 85% for speaker dependent
Transport Mode Detection 3 hours of data traces for three motorized modes (car, train, bus) Voltage peak based learning algorithm 85%
User Authentication 20 subjects Multi-Step Sparse Representation Classification 93%
Railway Trip Tracking 4 distinct train routes in the Sydney metropolitan area (36 trips including 360 data point) Ensemble classifier and a probabilistic-based trip inferring algorithm 97.2% for a journey of 7 stations
Audio Communication Receiver Series of experiments for varying distances between the speaker and the VEH receiver (2cm-100cm) and varying data rates (5bps to 35bps) ON-OFF keying (de)modulation scheme. 5-30 bps communication depending on the distance



KEH Modeler

This is a second-order mass spring damping system which is used to model an inertial harvester. It produces the amount of power that could be generated by an inertial harvester for a kinetic/ acceleration input.

  Mathworks Link  

KEH Recorder I


  External appearance           


Internal appearance                  


 KEH Recorded II

To be announced soon!



  1. G. Lan, W. Xu, S. Khalifa, M. Hassan, and W. Hu, “VEH-COM: Demodulating Vibration Energy Harvesting for Short Range Communication”, in proceedings of IEEE Percom 2017.
  2. Weitao Xu, Guohao Lan, Qi Lin, Sara Khalifa, Neil Bergmann, Mahbub Hassan, and Wen Hu , “KEH-Gait: Towards a Mobile Healthcare User Authentication System by Kinetic Energy Harvesting” , In Proceedings of the NDSS’17, San Diego, USA, February 26 – March 1, 2017.
  3. Marzieh Jalal Abadi, Sara Khalifa, Salil S Kanhere, and Mahbub Hassan, ” Energy Harvesting Wearables Can Tell Which Train Route You Have Taken”, in the 10th IEEE LCN Workshop On User MObility and VEhicular Networks, Dubai UAE, November 7-10, 2016.
  4. S. Khalifa, M. Hassan, and A. Seneviratne, “Feasibility and Accuracy of Hotword Detection using Vibration Energy Harvester”, accepted in WoWMoM 2016, Coimbra, Portugal, June 21-24, 2016.
  5. S. Khalifa, G. Lan, M. Hassan and W. Hu, “A Bayesian Framework for Energy-Neutral Activity Monitoring with Self-Powered Wearable Sensors”, the 12th IEEE PerCom Workshop on Context and Activity Modeling and Recognition, Sydney, Australia, 14-18 March 2016.
  6. G. Lan, W. Xu, S.Khalifa, M. Hassan, and Wen Hu, “Transportation Mode Detection Using Kinetic Energy Harvesting Wearables”, in proceedings of IEEE Percom WiP2016, Sydney, Australia, 14-18 March, 2016.
  7. S. Khalifa, M. Hassan, A. Seneviratne, and S. K. Das, “Energy harvesting wearables for activity-aware services,” IEEE Internet Computing, vol. 19, no. 5, pp. 8–16, 2015 (Impact factor :2.000)
  8. S. Khalifa, M. Hassan, and A. Seneviratne, “Step detection from power generation pattern in energy-lharvesting wearable devices”, in proceedings of IEEE iThings 2015, Sydney, Australia, 11-13 December, 2015.
  9. G. Lan, S. Khalifa, M. Hassan, and W. Hu, “Estimating calorie expenditure from output voltage of piezoelectric energy harvester – an experimental feasibility study,” in Proceedings of the 10th EAI International Conference on Body Area Networks (BodyNets), Sydney, Australia, 28-30 September, 2015
  10. S. Khalifa, M. Hassan, and A. Seneviratne, “Pervasive Self-powered Human Activity Recognition without the Accelerometer”, in proceedings of the (Percom 2015), St Louis, Missouri, USA, March 23-27, 2015 (Acceptance rate 14.7%)