Sensors -Sensor Data Segmentation -Data Segment (Choosing Low-Power Sensors (Based on Pause between Actions) Linear Accelerometer Gyroscope Activity Sensing (Recognizing the activities using state machine) Gravity sensor Set A Set B State Motionless Arm Lifting Up Mobile Rotating Energy Saving (Applying Different Schemes Walking Fine-Tuning pased on States) Maximum Body Jogging Arm Laying Down Shooting Energy Saved Medium Arm Energy Saved Body Arm Wrist Minimum Energy Saved Wrist Fig.3.System Architecture According to the above observations,we can utilize the low energy-consuming built-in sensors of the phone to detect z-axis the user's activities and reduce the energy consumption of taking photos.A simple example could be turning off the screen,decreasing the brightness of the screen,or decreasing the preview frame rate of the camera to reduce the energy cost when we find the user is not taking a photo. (a)Coordinates of Mo-(b)hold horizontally (c)hold horizontally C.System Architecture tion Sensors naturally backward The architecture of our system is shown in Figure 3. Fig.4.Coordinates of Phone and Direction of Phone Hold Firstly,we mainly obtain the data from low power-consuming built-in sensors,i.e.,the linear accelerometer,the gyroscope and the gravity sensor,as shown in the "Sensor"module. Arm level:It includes lifting the arm up and laying Secondly,we separate the data into different regions,which the arm down.The relationship between the data of are corresponding to the users'actions,as shown in the gravity sensor and linear accelerometer is used to "Activity Sensing"module.Thirdly,we adaptively adopt an distinguish the two actions.And voting mechanism appropriate energy-saving scheme for each action,as shown is used to guarantee the accuracy in the "Energy Saving"module.In the following paragraphs, we briefly introduce how we can realize the activity sensing Wrist level:It includes rotating the phone,making and reduce the power consumption. fine-tuning,and shooting a picture.We make use of 1)Activiry Sensing:Based on section II-A,the user's ac- a linear SVM model to distinguish them with the tions can be categorized into three parts:body movement,arm variance,mean,maximum and minimum of three movement,wrist movement.Correspondingly,in our system axises of three sensors as its features. architecture,we call the above parts as body level,arm level, wrist level,respectively.In each level,there may be more than 2)Energy-saving Scheme:Based on the feature and energy one action.Besides.the different levels may exist some transfer consumption in each action/state,we propose an adaptive relations.Therefore,we use the State Machine to describe the energy-saving scheme for taking photos.For example,when specific actions of the user.In the State Machine,each action you walk,jog or stay motionless,it's unnecessary to keep the is represented as a state.The transferable relations between screen on.When you lift your arm up,it's better to turn on the states are shown in Fig.3.Before we determine the type the screen and adjust the screen's brightness based on the light of the action,we first estimate which level the action belongs conditions.When you make fine-tuning to observe the camera to.Then,we further infer the specific action of the user based view before shooting a picture,it's better to make the camera on more sensor information. work on the preview state.In this way,we can make the context aware energy-saving schemes for the camera phones. Body level:It includes motionless.walking and jog- ging.Motionlessness can be recognized with its low variance of linear accelerometer's data.Then walk- III.SYSTEM DESIGN ing and jogging are distinguished with the frequency which can be calculated using Fast Fourier Transfor- In this section,we present the design of our energy-saving mation. scheme for smart camera phones based on activity sensing. 66                                !"#  $     %  &     ' # (    # (     # (    (   )*$ !+, ) + #  ,      )   $   ,   )  - $    #   $ , Fig. 3. System Architecture According to the above observations, we can utilize the low energy-consuming built-in sensors of the phone to detect the user’s activities and reduce the energy consumption of taking photos. A simple example could be turning off the screen, decreasing the brightness of the screen, or decreasing the preview frame rate of the camera to reduce the energy cost when we find the user is not taking a photo. C. System Architecture The architecture of our system is shown in Figure 3. Firstly, we mainly obtain the data from low power-consuming built-in sensors, i.e., the linear accelerometer, the gyroscope and the gravity sensor, as shown in the “Sensor” module. Secondly, we separate the data into different regions, which are corresponding to the users’ actions, as shown in the “Activity Sensing” module. Thirdly, we adaptively adopt an appropriate energy-saving scheme for each action, as shown in the “Energy Saving” module. In the following paragraphs, we briefly introduce how we can realize the activity sensing and reduce the power consumption. 1) Activity Sensing: Based on section II-A, the user’s ac￾tions can be categorized into three parts: body movement, arm movement, wrist movement. Correspondingly, in our system architecture, we call the above parts as body level, arm level, wrist level, respectively. In each level, there may be more than one action. Besides, the different levels may exist some transfer relations. Therefore, we use the State Machine to describe the specific actions of the user. In the State Machine, each action is represented as a state. The transferable relations between the states are shown in Fig. 3. Before we determine the type of the action, we first estimate which level the action belongs to. Then, we further infer the specific action of the user based on more sensor information. • Body level: It includes motionless, walking and jog￾ging. Motionlessness can be recognized with its low variance of linear accelerometer’s data. Then walk￾ing and jogging are distinguished with the frequency which can be calculated using Fast Fourier Transfor￾mation. x-axis y-axis z-axis top (a) Coordinates of Mo￾tion Sensors top (b) hold horizontally naturally top (c) hold horizontally backward Fig. 4. Coordinates of Phone and Direction of Phone Hold • Arm level: It includes lifting the arm up and laying the arm down. The relationship between the data of gravity sensor and linear accelerometer is used to distinguish the two actions. And voting mechanism is used to guarantee the accuracy. • Wrist level: It includes rotating the phone, making fine-tuning, and shooting a picture. We make use of a linear SVM model to distinguish them with the variance, mean, maximum and minimum of three axises of three sensors as its features. 2) Energy-saving Scheme: Based on the feature and energy consumption in each action/state, we propose an adaptive energy-saving scheme for taking photos. For example, when you walk, jog or stay motionless, it’s unnecessary to keep the screen on. When you lift your arm up, it’s better to turn on the screen and adjust the screen’s brightness based on the light conditions. When you make fine-tuning to observe the camera view before shooting a picture, it’s better to make the camera work on the preview state. In this way, we can make the context aware energy-saving schemes for the camera phones. III. SYSTEM DESIGN In this section, we present the design of our energy-saving scheme for smart camera phones based on activity sensing. 66