A presentation i did for the Ubiquitous and Wearable Computing seminar during my senior year in university. The presentation introduces many research papers on the field then discusses one of them thoroughly.

1. LOW-POWER INNOVATIVE
TECHNIQUES
MULTIPLE LOW-POWER APPROACHES, GDM LOW-POWER MANAGEMENT
FOR PERIODIC ACTIVITY MONITORING

2. 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

3. MOTIVATION

4. INTRODUCTION
• Internet of Things (IoT), ubiquitous and wearable
computing fields are evolving rapidly.
• Design Challenges: Size, Cost and Power.
• Wireless Charging.
• Kinetic Energy.

5. INTRODUCTION
CHALLENGES & 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.

6. BATTERIES
• Cost
• Behavioral factors:
• Temperature.
• Self Discharge.
• Memory Effect.
• Environmental factors:
• Leakage, gassing, toxicity.
• Shock resistance.

7. 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.

8. AN ULTRA-WEARABLE, WIRELESS, LOW POWER
ECG 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.

9. 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.

10. 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%.

11. 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.

12. 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

13. 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.

14. 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.

15. ARCHITECTURE

16. 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.

17. ARCHITECTURE

18. 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.

19. 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.

20. 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.

21. 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.

22. 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

23. EXPERIMENTAL RESULTS

24. POWER SAVING

25. 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.

26. 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.

27. 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).

28. 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.

29. 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

30. QUESTIONS ?