LOW-POWER INNOVATIVE TECHNIQUES

MULTIPLE LOW-POWER APPROACHES, GDM LOW-POWER MANAGEMENT

FOR PERIODIC ACTIVITY MONITORING

OUTLINE

• Motivation

• Introduction

• Current Research Papers

• Objective & challenges

• Granular Decision Making (GDM)

• Architecture

• Example

• Experimental results

• Conclusion and Further Discussions

• Future of The Future !

• References

MOTIVATION

INTRODUCTION

• Internet of Things (IoT), ubiquitous and wearable

computing fields are evolving rapidly.

• Design Challenges: Size, Cost and Power.

• Wireless Charging.

• Kinetic Energy.

INTRODUCTIONCHALLENGES & TECHNIQUES

• Energy is the most critical resource in a battery operated

device (ex. sensor).

• Radio interface consumes the most energy

• Ratio of energy requirements of CPU / radio interface

E(1 Instruction of CPU) : E(Sending of 1 bit) ≈1:1500 – 1:2900

Eradio = (P(per Bit)* Number of Bits)+ (I sleep* V * T)

• GPS is the worst sensor in power consumption.

• 6LoWPAN

• Bluetooth low energy (LE) by Nokia Research Centre (Wibree).

• Nike+ wireless technology by Nike and Apple.

BATTERIES

• Cost

• Behavioral factors:

• Temperature.

• Self Discharge.

• Memory Effect.

• Environmental factors:

• Leakage, gassing, toxicity.

• Shock resistance.

RESEARCH PAPERS

• Mohammad-Mahdi Bidmeshki, Roozbeh Jafari, “Low Power Programmable Architecture for Periodic Activity Monitoring”, The University of Texas at Dallas, April 2013.

• Cohn, Gabe, et al. "An ultra-low-power human body motion sensor using static electric field sensing." Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012.

• Chen, Chih-Yuan, et al. "A low-power bio-potential acquisition system with flexible PDMS dry electrodes for portable ubiquitous healthcare applications."Sensors 13.3 (2013): 3077-3091.

• Park, Chulsung, et al. "An ultra-wearable, wireless, low power ECG monitoring system." Biomedical Circuits and Systems Conference. BioCAS. IEEE, 2006.

• Cho, Moon-Haeng, and Cheol-Hoon Lee. "A low-power real-time operating system for ARC (actual remote control) wearable device." Consumer Electronics, IEEE Transactions on 56.3 (2010): 1602-1609.

• Gao, Yuan, et al. "Low-power ultrawideband wireless telemetry transceiver for medical sensor applications." Biomedical Engineering, IEEE Transactions on58.3 (2011): 768-772.

AN ULTRA-WEARABLE, WIRELESS, LOW POWERECG MONITORING SYSTEM

• Since the most power hungry component in

a wireless monitoring system is the wireless

transceiver.

• Using a low power wireless node can provide

a simple solution to such power issue.

• In this paper, the low power transceiver

inside “Eco” consumes 10 mA in

transmission mode (1Mbps, 0dBm) and 22

mA in receiving mode.

LOW-POWER ULTRAWIDEBAND WIRELESS TELEMETRY

• Impulse radio- ultrawideband (IR-UWB) communication transmits data using a short pulse of few nanoseconds.

• Transceiver can achieve low power by turning on only during pulse transmission.

• This makes transceiver power consumption scalable with data rate.

• So, High energy efficiency can be achieved over a wide range of data rates.

• The transmitter consumes an average power of

0.35 mW.

A LOW-POWER REAL-TIME OPERATING SYSTEM FOR ARC

• To solve the problem of hardware constraints, wearable computers must use small and low-power RTOS.

• In this paper, a new low-power RTOS designed specifically for active remote control (ARC) wearable device.

ARC is a wearable wristwatch-type universal remote control and is based on a 3-axis accelerometer sensor to recognize forearm gestures.

Experimental results showed that the proposed RTOS could achieve energy savings up to 47%.

AN ULTRA-LOW-POWER HUMAN BODY MOTION SENSOR USING STATIC ELECTRIC FIELD SENSING • In this paper, an ultra low-power approach for passively

sensing body motion using static electric fields, lowering power requirement by orders of magnitude.

• The application used here to infer the amount and type of body motion anywhere on the body.

• Their approach of sensing user’s movement builds on the work in the space of electric field (EF) sensing used in Human Computer Interaction.

• Lowest power commercially available accelerometers consume 400-100 µW and latest research device 36 µW.

• The sensors consume only 3.3 µW, and wake-up detection consumes another 3.3 µW, totaling 6.6 µW.

GDM LOW-POWER MANAGEMENT FOR PERIODIC ACTIVITY MONITORING

• Real-time sensing of human body movements has many applications in healthcare and wellness assessment.

• Using real-time activity monitoring and classification, special events can be captured.

• Body Sensor Networks (BSNs) provide such functionality.

• By placing these tiny nodes on different parts of the body, it can monitor every health related event.

• Sensor nodes equipped with inertial sensors can naturally capture human body movements.

• Major Challenges: Power, Battery size

OBJECTIVE & CHALLENGES

• Create batteryless units which can use body movements, heat as a source of energy.

• Challenge: power budget of such sources in the order of µW, current microcontrollers still require few mW or hundreds of µW.

• ASIC design can satisfy this power requirement but limited.

• The Granular Decision Making (GDM) architecture was proposed to perform less extensive but very low power signal processing.

GRANULAR DECISION MAKING (GDM)

• If signal is an immediate reject, GDM won’t activate remaining signal processing modules.

• If a signal is likely of interest, GDM increases the decision accuracy and power to make more confident decisions.

• Processing modules of GDM is called Screening Blocks

• A microcontroller can be used at the bottom level to thoroughly process the signal.

• If no processing needed, GDM can enable data recording/forwarding mechanism.

• This allows GDM to prevent the higher cost processing of non-target signals.

• This approach will provide a signal processing satisfies the µW power budget.

ARCHITECTURE

ARCHITECTURE

• The proposed architecture’s main feature is to reject non-target activities with a very low power cost.

• GDM architecture is based on wavelet extracted features and mainly applicable to dynamic and periodic activities.

• Tunable parameters are:

the number of features

Level of wavelet packet decomposition in which the features are computed

• Power consumption is directly related to these two parameters in terms of processing.

ARCHITECTURE

EXAMPLE

• Assume we are sampling a quantity like acceleration continuously and process it using a window (buffer) of size n. Wavelet packet transform is used to decompose the signal (window) up to J= log2 𝑛 levels.

• Fig.1 shows wavelet decomposition tree for signals (window) of length 16 up to level log2 16=4

• Local Discriminant Bases (LDB) was used to best represent the discrimination of signals (e.g., dashed boxes in Fig.1)

• To reduce number of features for the discrimination task (same length), statistical measures such as Fisher’s class separability measure was used to find the strength of each feature.

• Then fewer most powerful individual bases (features) in LDB are selected for the discrimination task.

EXAMPLE (CONT.)

• By experimenting on real inertial data, it shows that often using more features of higher levels can produce more accurate results but will have higher cost.

• To compute an individual base at level j+1, corresponding bases at j are required.

• The above property is used to build a hierarchical architecture that aims to reject non-target actions at the lowest possible computation cost.

• Using robust fisher’s measure, we find up to Ki most powerful individual bases at level i. Then decision making modules are made at level I which use k= 1,2..Ki most powerful bases for accepting or rejecting a signal.

• The decision making modules are called Screening Blocks Bi,k and have different costs (power consumption), as they use different number of features and extract features from various levels.

ARCHITECTURE

• A proposed methodology was made to select a path of screening blocks that reduces overall cost.

• To remove computation redundancy, a screening block may get features from previous blocks if it using features of same level.

• Each screening block processes the signal and if it confirms that it’s likely useful, it triggers the next screening block.

• This approach reduces the cost of processing non-target signals by removing them early.

EXPERIMENTAL RESULTS

• Measuring power consumption of proposed architecture, it should consider implementation details of architecture and most important the characteristics of the data and sensor readings obtained through BSNs.

• It’s crucial to specify the activation freq. of screening blocks, as it has significant effect the overall power consumption.

• Switching activity annotations was used to get the power consumption of each screening block as in Table 1.

EXPERIMENTAL RESULTS

• To get the inertial data of the activities, four subjects were used in the experiments and were asked to perform a set of periodic movements and non-periodic movements.

• 5% of periodic movements from table 2.

• Each subject wore 5 sensor nodes.

• The data for each movement were located for 30 seconds at 25Hz sampling rate and 12 bits resolution

EXPERIMENTAL RESULTS

POWER SAVING

CONCLUSION

• The proposed GDM architecture to discriminate periodic activities for use in BSN applications and uses wavelet extracted features to reject non-target actions early to reduce the need for expensive processing.

• On average, 75.7% power saving was obtained while maintaining 96.9% sensitivity on real motion data from several activities.

• For future work, The effect of other parameters such as sampling frequency, bit resolution, windows size and wavelet type on the accuracy, complexity shall be investigated.

• Detection of some actions may require data from multiple nodes, data fusion from multiple nodes shall be considered in future work too.

FURTHER DISCUSSIONS

• For Low-power ECG, Future work includes tighter integration of QUASAR’s sensor and improving both power efficiency and wireless performance

• For Low-power RTOS, Future work might include further adjustment of the proposed RTOS for other wearable applications.

• In addition, authors would like to explore power-aware OLED and memory-aware low-power techniques for wearable consumer market.

• For human body motion sensor using static electric field, plenty of applications for this approach is ideally suited like FitBit as its sensitivity to footsteps makes it ideal for pedometer-based physiological calorimetry.

• Also, a correlation between their signal and accelerometer have been shown which consumed 1-2 orders of magnitude more power than their proposed approach.

FURTHER DISCUSSIONS [CONT.]

• Future work for this approach is going to improve the hardware of the sensors used

• Authors claim they could still dramatically reduce the power consumption of the front-end hardware by implementing a custom analog IC.

• Although power consumption is already very low, it was implemented using higher bandwidth commercially off-the shelf parts.

• And despite the signal has already low bandwidth of 10 Hz, they estimate that if a custom analog IC was integrated to their system, it will consume between 1 and 10 nW(about 3 order of magnitude lower power than their existing approach).

FUTURE OF THE FUTURE !!

• Although most of the mentioned approaches are great, but still they are

still using the same non-renewable energy resources.

• Low-power is not needed by wearable computing only but also and most

importantly the Environment.

• Researchers and big companies all over the world are searching and

researching on other future renewable resources.

• Apple is trying to buy the idea of super-capacitor graphene.

• OLED applications such as OLED displays also is being

investigated on researches for the wearable computing industry.

REFERENCES

• Mohammad-Mahdi Bidmeshki, Roozbeh Jafari, “Low Power Programmable Architecture for Periodic Activity Monitoring”, The University of Texas at Dallas, April 2013.

• Cohn, Gabe, et al. "An ultra-low-power human body motion sensor using static electric field sensing." Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012.

• Park, Chulsung, et al. "An ultra-wearable, wireless, low power ECG monitoring system." Biomedical Circuits and Systems Conference. BioCAS. IEEE, 2006.

• Cho, Moon-Haeng, and Cheol-Hoon Lee. "A low-power real-time operating system for ARC (actual remote control) wearable device." Consumer Electronics, IEEE Transactions on 56.3 (2010): 1602-1609.

• Gao, Yuan, et al. "Low-power ultrawideband wireless telemetry transceiver for medical sensor applications." Biomedical Engineering, IEEE Transactions on58.3 (2011): 768-772.

• http://www.nature.com/ncomms/journal/v4/n2/full/ncomms2446.html

• http://en.wikipedia.org/wiki/OLED

QUESTIONS ?