A Quantitative Investigation of Inertial Power Harvesting

for Human-powered Devices

Jaeseok Yun Shwetak Patel MattReynolds Gregory Abowd

† School of InteractiveComputing & GVU Center

College of ComputingGeorgia Institute of

TechnologyAtlanta, GA 30332 USA

{jaeseok,abowd}@cc.gatech.edu

§ Electrical & ComputerEngineering

Pratt School of Engineering

Duke UniversityDurham, NC 27708 USA

matt.reynolds@duke.edu

‡ Computer Science &Engineering

Electrical EngineeringUniversity of WashingtonSeattle, WA 98195 USA

shwetak@cs.washington.edu

† School of InteractiveComputing & GVU Center

College of ComputingGeorgia Institute of

TechnologyAtlanta, GA 30332 USA

{jaeseok,abowd}@cc.gatech.edu

Presented By: Lulwah Alkwai

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

Outline

• Power remains a key to unlocking the potential for a sustainable UBIcomp reality

• Power harvesting from natural and renewable sources is a longstanding research area• Little research into capturing energy from daily human

activity• Prior research done in laboratory settings of selected

activities

• Possibility of powering consumer electronics or self sustaining body sensor networks

Introduction

Continuous all activity study of inertial power harvester performance using eight untethered human subjects going about their ordinary lives

Untethered, wearable apparatus(8 participants):• 3 axis accelerometers• 80Hz sampling rate• 6 body locations• 24 hours continuous

collection periods• 3 days of continuous

collection periods

Introduction

• First principles numerical model• Developed with MATLAB Simulink• Using the most common type of inertial power

harvester: Velocity Damped Resonant Generator (VDRG)– Estimate of achievable performance– Available power to devices based on the development model– Used reasonable assumption about the size of the generator

that could fit in the devices to develop the model

Introduction

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• Paradiso and Starner: Extensive survey of the available energy sources to power mobile devices

• Amirtharajah: Generating power using a model human walking as a vibration source

• Mitcheson:• Presented architectures for vibration driven micro power generators

• Buren: Measured acceleration from 9 location on body of human objects and estimated the maximum

output power. The acceleration signals measured from standard walking motion on treadmill

• Rome: A vertical excursion of a load during walking and climbing

• Kuo: A knee brace generator that produced electricity

• Troster: A button sized solar powered node Possibility of energy harvesting through ordinary exposure to sunlight and indoor light

Related Work

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• Powering devices such as consumer electronics and self sustaining body sensor networks

Alternative to batteriesMonitoring human vital signs

• Main result of the paper:Whether the power garnered from daily human

motion can practically power the low power components for wearable electronics

Understanding of Wearable Devices

The required power for all electronics

Understanding of Wearable Devices

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• Inertial Generator Model• Works by the body acceleration imparting forces on a

proof mass

• Three Categories of Inertial Generators• VDRG(Velocity Damped Resonant Generator)• CDRG(Coulomb Damped Resonant Generator)• CFPG(Coulomb Force Parametric Generator)(VDRG is used since the internal displacement travel of a generator exceeds 0.5 mm)

Power Harvester Model

• Vibration driven generators represented as a Damped Mass Spring System:

m: proof mass K: spring constant D: damping coefficient y(t): displacement of the generator z(t): displacement between the proof mass and the generator t: time

Power Harvester Model

In this Damped mass spring system, the electrical energy generated is represented as the energy dissipated in the mechanical damper

Model and operating principle for the VDRG

Power Harvester Model

• Important parameters during simulation analysis:

• m: proof mass• K: spring constant• D: damping coefficient• Zmax: internal travel limit

(m and Zmax are limited by size and mass of the object that holds the generator)

Power Harvester Model

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• Wearable Data Collection Unit– Two Logomatic Serial SD data loggers• Sampling Rate of 80 Hz• Record each input as a time series file

– Six 3 axis Accelerometer• Accelerometer packaged in small container sealed

against dust or sweat

– Contained in small waist pack– Allow 24 hours of continuous operation

1-Experimental Setup

1-Experimental Setup

• 4 men, 4 women• 3 days (2 weekdays, 1 weekend)• Participants can take it off when needed

( sleep, shower)• Participants are asked to record their activates

and time on diary sheet

1-Experimental Setup

• High pass filtered measure acceleration signals• 0.05 Hz cutoff frequency

• Obtained displacement of the accelerometer through double integrating the acceleration dataset

• Feed the resulting displacement time series into the VDRG model

2-Processing of Acceleration Signals

2-Processing of Acceleration Signals

Lower body experience much more acceleration than upper body->more electrical energy converted from kinetic energy than upper body

3-Data Analysis Frequency Domain

Upper body especially the wrist contain energy at low frequencies ->Because has a higher degree of freedom when moving than the lower body

3-Data Analysis Frequency Domain

Largest peak in each spectrum occurs at approximately 1 or 2 Hz

3-Data Analysis Frequency Domain

The determine the dominant frequency:Top 20 frequency components ranked from highest to lowest from each

spectrum

3-Data Analysis Frequency Domain

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

2 g / 4.2 cm 36 g / 10 cm 100 g / 20 cm

No useful digital object mountable

on knee

1-Simulation Setup

Proof mass m and internal travel length Zmax

• Acceleration data split into 10 sec fragments:Ed: the energy dissipated in the damperF=Dz damping forceZ1, Z2: start and end position

The equation can be expanded as follows

The average power during time interval T=10 sec

VDRG built with a single axes with one of the axes of accelerometers. The preferred orientation is chosen on max output power

2-Power Estimation Procedure

The following procedure was developed for using the measured acceleration dataset, in combination with the VDRG model to estimate available power

2-Power Estimation Procedure

• Search for optimal D which maximums PAll other variables determined previously

• After D is chosen the generated electrical power can be estimated

3-Optimization and Result

3-Optimization and Result

– Typical Efficiency for Mechanical to electrical conversion is %20

– Energy Generated is storable– The direction of the power harvester is continuously aligned

with the axis that generates the maximum output

• Average Electrical Power Expected:

3-Optimization and Result

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

Outline

Discussion

The ratio of the electrical output power of the harvester to the required power for wearable electronics:• a watch hanging on the neck • a watch on the wrist• a phone hanging on the neck • a phone on the waist • a phone on the arm • a shoe on the ankle

The X-index represents the subjects (8 subjects * 3 days = 24 days)The Y-index represents the wearable electronics

Discussion

• Output power is insufficient to continuously run the higher demanding electronics (such as MP3 decoder chip):

Can be used to charge a battery for intermittent operation

Possibly charge a backup battery for when standard recharging options not available

• Low power electronics (such as the wristwatches) can be powered continuously

Discussion

• From the diary sheet of participants it is possible to find the generated power from certain activities

Discussion

Discussion

Phone scale harvester will generate 15 m W while running

->GPS chip can be powered

Discussion

Shoe harvester will generate 20 m W while running

->GPS chip can be powered

Discussion

Continuous availability of more than 60 μ W->

Large number of activates taking place

Outline

• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• Possible to increase power output with heavier proof mass

Balanced with increased strain from additional weight

• Use all three axis to generate powerHarvester with three mass spring systems

• Generalize the K/D measurement across subjects

• Adaptive Tuning of VDRG

Limitation and Future Work

Outline• Introduction• Related Work• Understanding of wearable devices• Power Harvester Model• Data Collection and analysis

1. Experimental Setup2. Processing of Acceleration Signals3. Data Analysis in Frequency Domain

• Simulation and result1. Simulation Setup2. Power Estimation Procedure3. Optimization and Results

• Discussion• Limitation and Future Work• Conclusions

• First 24 hour continuous study of inertial power harvester performance

• Analysis of the energy that can be garnered from 6 locations on the body

• Shown feasibility to continuously operate motion powered wireless health sensor

• Motion generated power can intermittently power devices such as MP3 players or cell phones

Conclusion

Thank you!