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WO2010078186A2 - Prédiction de valeur par défaut pour gestion d'énergie d'ensemble de capteurs - Google Patents

Prédiction de valeur par défaut pour gestion d'énergie d'ensemble de capteurs Download PDF

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Publication number
WO2010078186A2
WO2010078186A2 PCT/US2009/069380 US2009069380W WO2010078186A2 WO 2010078186 A2 WO2010078186 A2 WO 2010078186A2 US 2009069380 W US2009069380 W US 2009069380W WO 2010078186 A2 WO2010078186 A2 WO 2010078186A2
Authority
WO
WIPO (PCT)
Prior art keywords
sensors
sensor
default value
classifier
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2009/069380
Other languages
English (en)
Other versions
WO2010078186A3 (fr
Inventor
Matthai Philipose
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intel Corp
Original Assignee
Intel Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Intel Corp filed Critical Intel Corp
Publication of WO2010078186A2 publication Critical patent/WO2010078186A2/fr
Publication of WO2010078186A3 publication Critical patent/WO2010078186A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0204—Operational features of power management
    • A61B2560/0209—Operational features of power management adapted for power saving
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0204—Operational features of power management
    • A61B2560/0214—Operational features of power management of power generation or supply
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb

Definitions

  • Embodiments of the present invention relate to wearable sensors on various parts of the body and, more particularly, to power management of wearable sensors to extend battery life or recharge intervals.
  • a person In many fields, for example in the health fields and the gaming arts, there may be a need for a person to wear a number of sensors on various parts of their body. Such sensors may be used as wearable activity monitors. That is, devices that could monitor and report on the user's daily physical activity patterns.
  • a network of wearable sensors attached, for example, to a user's arms and/or legs may enable a new class of physical game that would allow people to interact with the game.
  • a racing game for example, could be controlled by how fast somebody can shuffle their feet up and down, or arm and leg movements could control a fighting game. This capability would be similar to systems that use a wireless joystick to control a PC game with the additional benefit that the physical sensors would enable a more realistic gamming experience.
  • Figure 1 is a diagram illustrating a user wearing a plurality of wearable body sensors
  • Figure 2 is a flow diagram illustrating the use of default value prediction to evaluate classifier variable according to one embodiment of the invention.
  • Described is a system to convert an arbitrary classifier based on multiple sensors into an equivalent classifier with reduced requirements for powering the associated sensors.
  • these sensors are battery powered (as part of a mobile system, for instance), this invention can reduce mean time to battery recharge/replacement.
  • Figure 1 shows a person 100 wearing a plurality of sensors.
  • The may be camera pendants 102, arm movement sensors 104, RFID tag readers 106, leg movement sensors 108, or a variety of other types.
  • power hungry sensors consume much energy processing uninteresting information to produce default values.
  • other low-power sensors in the ensemble may be able to predict that the high-power sensor is likely to yield a default value.
  • a light-level sensor with light-level threshold classifier could detect when a video camera is unlikely to detect faces because the surroundings are too dark.
  • a wrist-worn accelerometer with body-motion classifier could detect when the wearer's hand motions make him unlikely to pick up RFID-tagged objects.
  • Embodiments provide a way to use default-value predictions by low-power sensors to avoid default-value measurements by high-power sensors and thus reduce the overall power consumed by sensor ensembles. Because many high- power sensors produce default values most of the time, and it can cost orders of magnitude less power for low-power sensors to predict these values, this invention can reduce power consumed by sensor-based classification systems significantly. The invention also provides safeguards against the case where the default-value predictor is poor.
  • Embodiments comprise a system that may be run on a computer that that takes the following inputs at each time step:
  • a classifier C which represents the relationship between variables v1 ,...,vn, of which some observation variables s1 ,...,sm represent sensors. For instance, C may map variables LightLevel and VideoFrame into the Boolean variable FaceDetected. Of these variables LightLevel and Currentlmage represent sensors.
  • An ensemble Si ..., S m of sensors, where sensor S 1 is mapped to variable S 1 .
  • sensors photo-diode and video-camera may be mapped into variables LightLevel and Currentlmage.
  • a sensor may be a combination of software and hardware: for instance, a person-detector sensor may combine video hardware with person-detection software and return just the pixels denoting the person.
  • the default value predictor should be trainable using sensor data for default values of sensor S 1 , take less power than S 1 to execute, and return the probability pi that S 1 WiII generate a default value.
  • P 1 may be capable of being informed of a misprediction (to facilitate online re-estimation).
  • embodiments work by evaluating classifier C in each time step. All non-observation variables are treated as usual for classifier C. Any time observation variable s, needs to be processed:
  • predictors P 1 may be trained either online using the information from our system in step 2b (if such training can be done fast), or periodicially offline by replaying data with all sensors enabled to get a data stream with correct predictions.
  • decision box 202 it is determined whether or not v is an observation variable. If it is not, then v is evaluated in the usual way for classifier C 204. If v is an observation variable and associated with a sensor S, then it is determined in box 206 if v has a predictor P associated with it. If no, then v is set to the result of running sensor S 208, a cached value may be used if one exists.
  • v does have a predictor P
  • the system predicts a default value d, and lets the prediction confidence by p in box 210.
  • decision box 212 it is determined whether p is greater than threshold t for predictor P. If no, then v is set to the result of running sensor S 208, as before. If p is greater than threshold t. If yes, then the system samples the sensor uniformly from real numbers in the range 0...1 at 214. Thus, sensors that are part of a classifier are sampled only when necessary. If after sampling the sample is less than or equal to p in box 216, then v is set to the default value in box 218. If the sample is greater than p, then v is set to the result of running the sensor S in box 220. Thereafter, if v is equal to the default value for P in box 222, the predictor P is informed of a failed classification at box 224.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Pathology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Secondary Cells (AREA)
  • Power Sources (AREA)

Abstract

L'invention porte sur un système qui convertit un classifieur arbitraire fondé sur de multiples capteurs en un classifieur équivalent présentant des exigences réduites pour alimenter électriquement les capteurs associés. Lorsque ces capteurs sont alimentés par batterie (en tant que partie d'un système mobile, par exemple), cette invention peut réduire la durée moyenne avant recharge/remplacement de la batterie.
PCT/US2009/069380 2008-12-31 2009-12-23 Prédiction de valeur par défaut pour gestion d'énergie d'ensemble de capteurs Ceased WO2010078186A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US34794308A 2008-12-31 2008-12-31
US12/347,943 2008-12-31

Publications (2)

Publication Number Publication Date
WO2010078186A2 true WO2010078186A2 (fr) 2010-07-08
WO2010078186A3 WO2010078186A3 (fr) 2010-09-16

Family

ID=42310558

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2009/069380 Ceased WO2010078186A2 (fr) 2008-12-31 2009-12-23 Prédiction de valeur par défaut pour gestion d'énergie d'ensemble de capteurs

Country Status (1)

Country Link
WO (1) WO2010078186A2 (fr)

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7034677B2 (en) * 2002-07-19 2006-04-25 Smiths Detection Inc. Non-specific sensor array detectors
JP2004216125A (ja) * 2002-11-19 2004-08-05 Seiko Instruments Inc 生体情報検出端末制御システム
JP2006141902A (ja) * 2004-11-24 2006-06-08 Hitachi Ltd 安否確認装置、安否確認方法及び安否確認システム
JP4575133B2 (ja) * 2004-12-15 2010-11-04 日本電信電話株式会社 センシングシステムおよび方法

Also Published As

Publication number Publication date
WO2010078186A3 (fr) 2010-09-16

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