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Ayb 1 Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory An Automated.

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Presentation on theme: "Ayb 1 Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory An Automated."— Presentation transcript:

1 ayb 1 Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory Ari Y. Benbasat Responsive Environments Group MIT Media Laboratory An Automated Framework for Power-Efficient Detection in Embedded Sensor Systems

2 ayb 2 Introduction Embedded sensor nodes are being used in a variety of applications, such as:Embedded sensor nodes are being used in a variety of applications, such as:  Detecting the activities of housebound elders  Monitoring wildlife in remote regions  Tracking the state of smart assets in the supply chain Want to make sensor systems as power-efficient as possible to allow for a wider range of applicationsWant to make sensor systems as power-efficient as possible to allow for a wider range of applications While any (reasonable) power savings is beneficial, there are two key targets:While any (reasonable) power savings is beneficial, there are two key targets:  Mass-market applications  Infinite life (via parasitic power) Embedded sensor nodes are being used in a variety of applications, such as:Embedded sensor nodes are being used in a variety of applications, such as:  Detecting the activities of housebound elders  Monitoring wildlife in remote regions  Tracking the state of smart assets in the supply chain Want to make sensor systems as power-efficient as possible to allow for a wider range of applicationsWant to make sensor systems as power-efficient as possible to allow for a wider range of applications While any (reasonable) power savings is beneficial, there are two key targets:While any (reasonable) power savings is beneficial, there are two key targets:  Mass-market applications  Infinite life (via parasitic power)

3 ayb 3 Illustrative application: Gait Measurement System Gait changes can be symptoms of important medical conditionsGait changes can be symptoms of important medical conditions Uses simple external shoe attachment rather than full motion tracking labUses simple external shoe attachment rather than full motion tracking lab  Motion tracked with inertial sensors and tactile insole  Uses framework mentioned later Gait changes can be symptoms of important medical conditionsGait changes can be symptoms of important medical conditions Uses simple external shoe attachment rather than full motion tracking labUses simple external shoe attachment rather than full motion tracking lab  Motion tracked with inertial sensors and tactile insole  Uses framework mentioned later Validated against MGH gait laboratoryValidated against MGH gait laboratory High 100mW power drawHigh 100mW power draw Validated against MGH gait laboratoryValidated against MGH gait laboratory High 100mW power drawHigh 100mW power draw

4 ayb 4 Illustrative application: Commercial Potential Considerable interest in combining medical sensors with commonplace portable devicesConsiderable interest in combining medical sensors with commonplace portable devices Difficult to achieve because of power useDifficult to achieve because of power use e.g. consider (naively) adding the above inertial sensor unit (65mW) to familiar portable devices:e.g. consider (naively) adding the above inertial sensor unit (65mW) to familiar portable devices:  iPod Shuffle  Currently 67mW - power usage almost doubles  Motorola v60  Currently 20mW (standby) - power usage increase of >300% But user is most often standing still!But user is most often standing still! Considerable interest in combining medical sensors with commonplace portable devicesConsiderable interest in combining medical sensors with commonplace portable devices Difficult to achieve because of power useDifficult to achieve because of power use e.g. consider (naively) adding the above inertial sensor unit (65mW) to familiar portable devices:e.g. consider (naively) adding the above inertial sensor unit (65mW) to familiar portable devices:  iPod Shuffle  Currently 67mW - power usage almost doubles  Motorola v60  Currently 20mW (standby) - power usage increase of >300% But user is most often standing still!But user is most often standing still!

5 ayb 5 Overall Strategy First principles: reduce power usage of sensor nodes by reducing power usage of the sensors themselvesFirst principles: reduce power usage of sensor nodes by reducing power usage of the sensors themselves Achieve savings through high-level algorithms and designs rather than low-level technical changesAchieve savings through high-level algorithms and designs rather than low-level technical changes  System will alter its sensing based on the current state Benefits of reduced sensing cascade upwards:Benefits of reduced sensing cascade upwards:  Reduced data processing  Reduced storage/bandwidth needs  Reduced analysis for human experts First principles: reduce power usage of sensor nodes by reducing power usage of the sensors themselvesFirst principles: reduce power usage of sensor nodes by reducing power usage of the sensors themselves Achieve savings through high-level algorithms and designs rather than low-level technical changesAchieve savings through high-level algorithms and designs rather than low-level technical changes  System will alter its sensing based on the current state Benefits of reduced sensing cascade upwards:Benefits of reduced sensing cascade upwards:  Reduced data processing  Reduced storage/bandwidth needs  Reduced analysis for human experts

6 ayb 6 Power Breakdown: Field-Tested Applications

7 ayb 7 Overall Approach Three-part framework for the design and construction of power-efficient sensor systems: 1.A modular hardware system for prototyping of power- efficient sensor nodes 2.A technique for constructing state classifiers with parameterized power/accuracy trade-offs 3.An embedded implementation of the classifier including sensor management Three-part framework for the design and construction of power-efficient sensor systems: 1.A modular hardware system for prototyping of power- efficient sensor nodes 2.A technique for constructing state classifiers with parameterized power/accuracy trade-offs 3.An embedded implementation of the classifier including sensor management

8 ayb 8 Distinguishing Characteristics Our solution is: Algorithmically generatedAlgorithmically generated  Not ad-hoc ScalableScalable  Not limited size ResponsiveResponsive  Not static Our solution is: Algorithmically generatedAlgorithmically generated  Not ad-hoc ScalableScalable  Not limited size ResponsiveResponsive  Not static

9 ayb 9 Related Works: Processor/Transceiver (Historic) Power reduction concentrated on the processor and transceiverPower reduction concentrated on the processor and transceiver Gains were achieved through either:Gains were achieved through either:  Changing the rate of operation to complete the task just in time, or  Completing the task as quickly as possible and sleeping Laptops and PDAs use these techniquesLaptops and PDAs use these techniques Power reduction concentrated on the processor and transceiverPower reduction concentrated on the processor and transceiver Gains were achieved through either:Gains were achieved through either:  Changing the rate of operation to complete the task just in time, or  Completing the task as quickly as possible and sleeping Laptops and PDAs use these techniquesLaptops and PDAs use these techniques

10 ayb 10 Related Works: Sensing (Recent) Alter sampling rate based on measured dataAlter sampling rate based on measured data  Responds in purely entropic fashion, hence cannot specify which states are/n’t of interest. [Jain 04/Rahimi 04] Sentry nodes awaken slave nodes in networkSentry nodes awaken slave nodes in network  Slave nodes have fixed operation and do not use other information available to them. [He 04/Zhao 02] State detection through samplingState detection through sampling  Binary and hand-scripted [Dutta 05] Alter sampling rate based on measured dataAlter sampling rate based on measured data  Responds in purely entropic fashion, hence cannot specify which states are/n’t of interest. [Jain 04/Rahimi 04] Sentry nodes awaken slave nodes in networkSentry nodes awaken slave nodes in network  Slave nodes have fixed operation and do not use other information available to them. [He 04/Zhao 02] State detection through samplingState detection through sampling  Binary and hand-scripted [Dutta 05]

11 ayb 11 Test Application: Real-time Gait Analysis Want to distinguish the following motions:Want to distinguish the following motions:  Level gait  Walking up or down stairs  Walking up or down incline  Shuffling Physicians often interested in only one of these motions:Physicians often interested in only one of these motions:  Parkinson’s Disease: Information about shuffling  Total knee replacement: Information about stair climbing Want to distinguish the following motions:Want to distinguish the following motions:  Level gait  Walking up or down stairs  Walking up or down incline  Shuffling Physicians often interested in only one of these motions:Physicians often interested in only one of these motions:  Parkinson’s Disease: Information about shuffling  Total knee replacement: Information about stair climbing

12 ayb 12 Overall Approach Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation

13 ayb 13 Modular Hardware: Goals Encapsulate KnowledgeEncapsulate Knowledge  Set designs for common structures Simplify PrototypingSimplify Prototyping  Architecture allows designer to quickly construct applications ExpandabilityExpandability  New boards can be added without redesigning others Encapsulate KnowledgeEncapsulate Knowledge  Set designs for common structures Simplify PrototypingSimplify Prototyping  Architecture allows designer to quickly construct applications ExpandabilityExpandability  New boards can be added without redesigning others

14 ayb 14 Modular Hardware: Overall Board Design Sensor boards with a variety of sensorsSensor boards with a variety of sensors  Each with own signal processing  Sensors chosen to be low-power and fast wake up  Uses low power op amps and other components Microprocessor also controls the power to the individual sensorsMicroprocessor also controls the power to the individual sensors Sensor boards with a variety of sensorsSensor boards with a variety of sensors  Each with own signal processing  Sensors chosen to be low-power and fast wake up  Uses low power op amps and other components Microprocessor also controls the power to the individual sensorsMicroprocessor also controls the power to the individual sensors Sensors Analog Signal Processing Mux PP Select sensor Control power to sensor

15 ayb 15 Modular Hardware: Boards Processor board:Processor board:  Responsible for the data collection  TI MSP430 processor with analog to digital converter  Low-power, fast-wake sleep mode and hardware multiply Six-degree inertial measurement unit (IMU)Six-degree inertial measurement unit (IMU)  Measures motion in all three dimensions  Acceleration: two ADXL202 and a four-way static tilt switch (micropower/single-bit)  Angular velocity: two Murata ENC03J and a ADXRS300 gyroscopes (allows for planar package) Processor board:Processor board:  Responsible for the data collection  TI MSP430 processor with analog to digital converter  Low-power, fast-wake sleep mode and hardware multiply Six-degree inertial measurement unit (IMU)Six-degree inertial measurement unit (IMU)  Measures motion in all three dimensions  Acceleration: two ADXL202 and a four-way static tilt switch (micropower/single-bit)  Angular velocity: two Murata ENC03J and a ADXRS300 gyroscopes (allows for planar package)

16 ayb 16 Modular Hardware: Sample Uses of Platform Complete applications: FlexiGesture: Instrument that allows flexible assignment from input gesture to output sound.FlexiGesture: Instrument that allows flexible assignment from input gesture to output sound. Gait Shoe: Shoe mounted system for real- time measurement of subject motionGait Shoe: Shoe mounted system for real- time measurement of subject motion Used for prototyping: Huggable: Instrumented responsive plush bear to act as a companion animal in wide variety of settings.Huggable: Instrumented responsive plush bear to act as a companion animal in wide variety of settings. Complete applications: FlexiGesture: Instrument that allows flexible assignment from input gesture to output sound.FlexiGesture: Instrument that allows flexible assignment from input gesture to output sound. Gait Shoe: Shoe mounted system for real- time measurement of subject motionGait Shoe: Shoe mounted system for real- time measurement of subject motion Used for prototyping: Huggable: Instrumented responsive plush bear to act as a companion animal in wide variety of settings.Huggable: Instrumented responsive plush bear to act as a companion animal in wide variety of settings.

17 ayb 17 Test Application: Sensors and Test costs We collected data using a system made up of the processor board and the IMU boardWe collected data using a system made up of the processor board and the IMU board Data collected at 200HzData collected at 200Hz  Will downsample to mimic lower frequencies We collected data using a system made up of the processor board and the IMU boardWe collected data using a system made up of the processor board and the IMU board Data collected at 200HzData collected at 200Hz  Will downsample to mimic lower frequencies

18 ayb 18 Overall Approach Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation

19 ayb 19 Classification: Goals Determine the state as accurately and power- efficiently as possibleDetermine the state as accurately and power- efficiently as possible  System allows a trade-off between the power/accuracy Only sensors active are those needed to make a decision at a given pointOnly sensors active are those needed to make a decision at a given point Requires a minimum of effort from the application designerRequires a minimum of effort from the application designer Determine the state as accurately and power- efficiently as possibleDetermine the state as accurately and power- efficiently as possible  System allows a trade-off between the power/accuracy Only sensors active are those needed to make a decision at a given pointOnly sensors active are those needed to make a decision at a given point Requires a minimum of effort from the application designerRequires a minimum of effort from the application designer

20 ayb 20 Classification: Data Stream The application designer will provide representative sensor data streamsThe application designer will provide representative sensor data streams  Individual states (e.g. shuffling, climbing stairs) must be annotated System will respond only when it is determined to be in one of these statesSystem will respond only when it is determined to be in one of these states Classification algorithms will generate a state decision tree using this data streamClassification algorithms will generate a state decision tree using this data stream The application designer will provide representative sensor data streamsThe application designer will provide representative sensor data streams  Individual states (e.g. shuffling, climbing stairs) must be annotated System will respond only when it is determined to be in one of these statesSystem will respond only when it is determined to be in one of these states Classification algorithms will generate a state decision tree using this data streamClassification algorithms will generate a state decision tree using this data stream

21 ayb 21 Classification: Decision Trees Standard top down divide and conquer method for constructionStandard top down divide and conquer method for construction Each node specifies the information necessary to make the next decisionEach node specifies the information necessary to make the next decision Each leaf specifies the determined system stateEach leaf specifies the determined system state Standard top down divide and conquer method for constructionStandard top down divide and conquer method for construction Each node specifies the information necessary to make the next decisionEach node specifies the information necessary to make the next decision Each leaf specifies the determined system stateEach leaf specifies the determined system state Mean(A z ) <2009 Uphill walk Max(G z ) <1878 walk yes

22 ayb 22 Classification: Classifier Choice  Only certain sensors are used based on state  Hierarchical activation requires a hierarchical classifier. Can be easily converted into very compact microcontroller codeCan be easily converted into very compact microcontroller code  Very fast execution ( based on comparisons )  Only certain sensors are used based on state  Hierarchical activation requires a hierarchical classifier. Can be easily converted into very compact microcontroller codeCan be easily converted into very compact microcontroller code  Very fast execution ( based on comparisons ) Decision trees are used for the following reasons: Classification is structured as a sequence of queries:Classification is structured as a sequence of queries: Decision trees are used for the following reasons: Classification is structured as a sequence of queries:Classification is structured as a sequence of queries: Mean(A z ) <2009 Uphill walk Max(G z ) <1878 walk yes

23 ayb 23 Classification: Features Features used are a selection of simple functions :Features used are a selection of simple functions :  Windowed minimum, maximum, mean, variance  All constant time for incremental calculation (on average)  Calculated at a number of sampling rates Features used are a selection of simple functions :Features used are a selection of simple functions :  Windowed minimum, maximum, mean, variance  All constant time for incremental calculation (on average)  Calculated at a number of sampling rates

24 ayb 24 Classification: Tree Construction (1) Decision trees are constructed in a recursive top down functionDecision trees are constructed in a recursive top down function  All splitting points of all the features are tested  Best division is chosen based on a purity measure C(split)  Process continues with child nodes until all are pure (all one state) Decision trees are constructed in a recursive top down functionDecision trees are constructed in a recursive top down function  All splitting points of all the features are tested  Best division is chosen based on a purity measure C(split)  Process continues with child nodes until all are pure (all one state) Right child [p,n] [p 2,n 2 ][p 1,n 1 ] Number of positive (p) and negative (n) examples Left child Root node Sample Split

25 ayb 25 Classification: Test Costs For any given feature, the test cost is the total energy necessary to obtain the feature:For any given feature, the test cost is the total energy necessary to obtain the feature:  Test Cost = Test Cost (sensing) + Test Cost (features) Test Cost (sensing) is energy necessary to collect a sampleTest Cost (sensing) is energy necessary to collect a sample Test Cost (features) is the energy necessary to calculate the desired feature in the microcontrollerTest Cost (features) is the energy necessary to calculate the desired feature in the microcontroller  For active sensors, the sampling cost dominates  For passive sensors, the feature cost dominates Features already used in the tree are free (Test Cost = 0)Features already used in the tree are free (Test Cost = 0) For any given feature, the test cost is the total energy necessary to obtain the feature:For any given feature, the test cost is the total energy necessary to obtain the feature:  Test Cost = Test Cost (sensing) + Test Cost (features) Test Cost (sensing) is energy necessary to collect a sampleTest Cost (sensing) is energy necessary to collect a sample Test Cost (features) is the energy necessary to calculate the desired feature in the microcontrollerTest Cost (features) is the energy necessary to calculate the desired feature in the microcontroller  For active sensors, the sampling cost dominates  For passive sensors, the feature cost dominates Features already used in the tree are free (Test Cost = 0)Features already used in the tree are free (Test Cost = 0)

26 ayb 26 Classification: Tree Construction (2) Basic splitting criterion modified to take test cost into account:Basic splitting criterion modified to take test cost into account:  Basic form: where W is a parameter, which alters the power/accuracy trade-off Desire two modes of operation:Desire two modes of operation: 1.All activated sensors (Test Cost = 0) are equivalent 2.Unused sensors are weighted proportional to test cost Set  =1,  such that min  · (Test Cost) = 10Set  =1,  such that min  · (Test Cost) = 10 Max. useful value of W is set by range of C(split)Max. useful value of W is set by range of C(split) Basic splitting criterion modified to take test cost into account:Basic splitting criterion modified to take test cost into account:  Basic form: where W is a parameter, which alters the power/accuracy trade-off Desire two modes of operation:Desire two modes of operation: 1.All activated sensors (Test Cost = 0) are equivalent 2.Unused sensors are weighted proportional to test cost Set  =1,  such that min  · (Test Cost) = 10Set  =1,  such that min  · (Test Cost) = 10 Max. useful value of W is set by range of C(split)Max. useful value of W is set by range of C(split)

27 ayb 27 Test Application: Structure Decision trees were built to separate each individual motion from the other five:Decision trees were built to separate each individual motion from the other five:  Non-ambulatory (roughly: still) data was not used with this classifier W varied to create a population of classifiersW varied to create a population of classifiers Priors:Priors: Decision trees were built to separate each individual motion from the other five:Decision trees were built to separate each individual motion from the other five:  Non-ambulatory (roughly: still) data was not used with this classifier W varied to create a population of classifiersW varied to create a population of classifiers Priors:Priors:

28 ayb 28 Test Application: Comparison Support vector machines (SVM) were used for comparisonSupport vector machines (SVM) were used for comparison  Classify by dividing feature space using a linear or Gaussian kernel  All available features are used in every classification SVMs are not appropriate for real-time embeddedSVMs are not appropriate for real-time embedded  Too computationally expensive  Linear: Dot product of vectors with length of number of features  Gaussian: One dot product as above for each support vector SVM power usage was altered by constructing multiple classifiers, each with access to a subset of sensorsSVM power usage was altered by constructing multiple classifiers, each with access to a subset of sensors Support vector machines (SVM) were used for comparisonSupport vector machines (SVM) were used for comparison  Classify by dividing feature space using a linear or Gaussian kernel  All available features are used in every classification SVMs are not appropriate for real-time embeddedSVMs are not appropriate for real-time embedded  Too computationally expensive  Linear: Dot product of vectors with length of number of features  Gaussian: One dot product as above for each support vector SVM power usage was altered by constructing multiple classifiers, each with access to a subset of sensorsSVM power usage was altered by constructing multiple classifiers, each with access to a subset of sensors

29 Level Gait Descend Stairs Ascend Stairs Downhill GaitShuffling GaitUphill Gait

30 ayb 30 Test Application: Tree for Uphill Gait (1) Uphill walk SD(G z )<72 walk Mean(A z )<2000 Mean(Tilt)<4.8 W = Acc = Cost = 5.4mW

31 ayb 31 Test Application: Tree for Uphill Gait (2) Uphill walk Mean(A z ) <2000 walk SD(G z )<72 Mean(Tilt)< 4.8 walk W = Acc = Cost = 12.8mW

32 ayb 32 Test Application: Tree for Uphill Gait (3) walk SD(G z ) < 78 Mean(A z ) < 2000 Uphill walk Max(G y ) < 1880 W = Acc = Cost = 13.6mW

33 ayb 33 Overall Approach Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation Modular hardware Modular hardware Classification Algorithms Classification Algorithms Embedded Implementation Embedded Implementation

34 ayb 34 Embedded Software: Goals Conversion of particular decision tree classifier for use in a real-time systemConversion of particular decision tree classifier for use in a real-time system  Fairly compact implementation Software deals with two main timing issues:Software deals with two main timing issues:  Microscopic: Power-cycling of components within a single data collection cycle to save power  Macroscopic: Application of hysteresis to activity level transitions as a trade-off between power and latency Conversion of particular decision tree classifier for use in a real-time systemConversion of particular decision tree classifier for use in a real-time system  Fairly compact implementation Software deals with two main timing issues:Software deals with two main timing issues:  Microscopic: Power-cycling of components within a single data collection cycle to save power  Macroscopic: Application of hysteresis to activity level transitions as a trade-off between power and latency

35 ayb 35 Embedded Software: Main Loop Collect data from sensors current in useCollect data from sensors current in use Run classifier to determine state (or next sensor needed)Run classifier to determine state (or next sensor needed)  Respond to state (if desired by application designer) Handle sensor de/activation requestsHandle sensor de/activation requests Sleep until next cycleSleep until next cycle Collect data from sensors current in useCollect data from sensors current in use Run classifier to determine state (or next sensor needed)Run classifier to determine state (or next sensor needed)  Respond to state (if desired by application designer) Handle sensor de/activation requestsHandle sensor de/activation requests Sleep until next cycleSleep until next cycle

36 ayb 36 Embedded Software: Sampling Cycle Active sensors are awakened in reverse order of wake up timeActive sensors are awakened in reverse order of wake up time  Sensors with long wake-up times cannot be duty-cycled Active sensors are awakened in reverse order of wake up timeActive sensors are awakened in reverse order of wake up time  Sensors with long wake-up times cannot be duty-cycled Actions Parts Waking up Active

37 ayb 37 Embedded Software: Sensor Activation Small glitches in the data stream can change the current node of the decision treeSmall glitches in the data stream can change the current node of the decision tree  Addition of latencies allows system to smooth over small glitches Gap in sampling prevents windowed features from being calculated for a full cycleGap in sampling prevents windowed features from being calculated for a full cycle  Turn off latency is set long to take this into account Small glitches in the data stream can change the current node of the decision treeSmall glitches in the data stream can change the current node of the decision tree  Addition of latencies allows system to smooth over small glitches Gap in sampling prevents windowed features from being calculated for a full cycleGap in sampling prevents windowed features from being calculated for a full cycle  Turn off latency is set long to take this into account Sensor needed  Sensor not needed 

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39 ayb 39 Distinguishing Characteristics Our solution is: Algorithmically generatedAlgorithmically generated  Not ad-hoc ScalableScalable  Not limited size ResponsiveResponsive  Not static Our solution is: Algorithmically generatedAlgorithmically generated  Not ad-hoc ScalableScalable  Not limited size ResponsiveResponsive  Not static

40 ayb 40 Contributions Designed an automated framework to construct power- efficient state detection systemsDesigned an automated framework to construct power- efficient state detection systems  A modular hardware platform, incorporating a number of low-power design techniques, to aid in the construction of embedded sensors.  Modified the standard decision tree training algorithm to add a parameterized weighting relative to the cost of the features.  Showed this parameter creates a population of trees at different points on the power/accuracy plane.  In a complex sample application, these classifiers were shown to generally perform better on the cost/accuracy plane than support vector machines Designed an automated framework to construct power- efficient state detection systemsDesigned an automated framework to construct power- efficient state detection systems  A modular hardware platform, incorporating a number of low-power design techniques, to aid in the construction of embedded sensors.  Modified the standard decision tree training algorithm to add a parameterized weighting relative to the cost of the features.  Showed this parameter creates a population of trees at different points on the power/accuracy plane.  In a complex sample application, these classifiers were shown to generally perform better on the cost/accuracy plane than support vector machines

41 ayb 41 Future Work (1) Ability to add new features to classifierAbility to add new features to classifier  Exploit knowledge of application and structure of data Tree pruning algorithm to allow accuracy / latency tradeoffTree pruning algorithm to allow accuracy / latency tradeoff  New sensors sometimes provide minimal increase in accuracy Add top-level trigger variablesAdd top-level trigger variables  Binary decision which wakes system Ability to add new features to classifierAbility to add new features to classifier  Exploit knowledge of application and structure of data Tree pruning algorithm to allow accuracy / latency tradeoffTree pruning algorithm to allow accuracy / latency tradeoff  New sensors sometimes provide minimal increase in accuracy Add top-level trigger variablesAdd top-level trigger variables  Binary decision which wakes system

42 ayb 42 Future Work (2) Increased automation to simplify the task of going from hardware to classifier to softwareIncreased automation to simplify the task of going from hardware to classifier to software  Currently requires a lot of manual labeling and selection Addition of active sensors (such as radar/sonar) to the frameworkAddition of active sensors (such as radar/sonar) to the framework  Various power levels alter operation of device Extend framework to sensor networksExtend framework to sensor networks  State or data from nearby nodes could be included in decision tree Increased automation to simplify the task of going from hardware to classifier to softwareIncreased automation to simplify the task of going from hardware to classifier to software  Currently requires a lot of manual labeling and selection Addition of active sensors (such as radar/sonar) to the frameworkAddition of active sensors (such as radar/sonar) to the framework  Various power levels alter operation of device Extend framework to sensor networksExtend framework to sensor networks  State or data from nearby nodes could be included in decision tree

43 ayb 43 Acknowledgements Joe ParadisoJoe Paradiso Rosalind Picard and Mani SrivastavaRosalind Picard and Mani Srivastava ResEnvResEnv Synthetic CharactersSynthetic Characters Tangible Media and Aesthetics & ComputationTangible Media and Aesthetics & Computation The Academic Office and our Administrative SupportThe Academic Office and our Administrative Support My friends inside (and out!) of the LabMy friends inside (and out!) of the Lab And of course, My parentsMy parents Joe ParadisoJoe Paradiso Rosalind Picard and Mani SrivastavaRosalind Picard and Mani Srivastava ResEnvResEnv Synthetic CharactersSynthetic Characters Tangible Media and Aesthetics & ComputationTangible Media and Aesthetics & Computation The Academic Office and our Administrative SupportThe Academic Office and our Administrative Support My friends inside (and out!) of the LabMy friends inside (and out!) of the Lab And of course, My parentsMy parents


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