There are two key technical challenges in realizing the consistent approach,regardless of the exact orientation of the activity sensing scheme.The first challenge is to realize the human bodies.3)We have implemented a prototype system to activity sensing in a user-independent approach,such that the evaluate the real performance,the experiment results in real derived recognition model can be extended to recognize the settings shows that our meta-activity recognition achieves an activities of any arbitrary human subjects,regardless of the average accuracy of 92%for user-independent activity sensing. detailed differences and inherent deviations in the activities from different human subjects.To address this challenge,we II.PROBLEM FORMULATION propose to leverage the angle profiles,i.e.,the angles between In this paper,we investigate the wearable approach for the specified limb and the coordinate axes,to depict the limb activity sensing,i.e.,a wearable device is worn by the human movements.The angle profiles are able to capture the angle subject to continuously collect the inertial measurements of variation of the limb movements relative to the human body, human motion,then an activity sensing scheme is required to which tackle the deviation details caused by the user-specific accurately recognize the complex activities of limb movements characters like the height.Moreover,we propose the method of from human subjects.The complex activity refers to the kind "meta-activity recognition"to perform activity sensing in the of activity with a large range of movement,such as sit-ups logical representation level,based on the sequence of meta- and dumbbell lateral raise.Without loss of generality,we activity profiles,so as to tackle the variations in the long se- leverage the smart watch to sense the human motions,which quence of small-range activities.Specifically,according to the is embedded with inertial sensors including the accelerometer, inertial measurements collected from human motion,instead of gyroscope and magnetometer. performing the waveform-based matching like dynamic time In this paper,we aim to design an activity sensing scheme, warping,we decompose the complex activity into a sequence by considering the following metrics in system performance: of meta-activities,and use this sequence to recognize the 1)Accuracy:The expected accuracy for the activity sensing complex activity via the least edit distance-based matching. scheme to successfully match a specific activity to a correct The second challenge is to build a consistent scheme to activity should be greater than a specified threshold,e.g.,85%. depict the human motion according to the inertial measure- 2)Time-efficiency:The time delay of the activity recognition ments from the wearable devices.Since the human subjects process should be less than a specified threshold,e.g.,500ms. may perform the activities towards any arbitrary direction 3)User-independence:When performing activity sensing,no during the human motion,this causes the templates for activity training data should be required from the specified user. recognition to depend heavily on the actual direction the 4)Lightweight-training:The essential quantity of the training human body is facing,and further enhances the complexities samples to build the templates should be small enough.. in performing activity sensing due to the inconsistency.To III.MODELING THE HUMAN MOTION address this challenge,we depict all the inertial measurements of human motion in terms of a body coordinate system in A.Coordinate System Transformation a consistent approach.Specifically,according to the gravity In regard to activity sensing,as the raw inertial measure- direction and the magnetic direction extracted from the inertial ments are collected from the embedded inertial sensors in measurements.we transform the measurements from the watch the smart watch,they are measured by reference to the body coordinate system (WCS)to the global coordinate system frame of the smart watch.However,the watch coordinate (GCS).Then,by specifying two signal gestures,i.e.,extending system is continuously changing with the arm/wrist movement the arm to the front and dropping the arm downward,we can during the process of human motion,thus the measurements figure out the orientation of the human body in the global from the watch coordinate system cannot be used as a stable coordinate system according to the measurements in the signal reference for the specified activities.In fact,since the human gestures,thus we further transform the measurements to the subject may be performing the activity towards any arbitrary body coordinate system (BCS). direction,the movements should be depicted as the movement To the best of our knowledge,this paper presents the of arms or legs relative to the human body,regardless of the first study of using the method "meta-activity recognition" absolute moving direction of the limbs.Therefore,in order to for logical cognition-based activity sensing.Specifically,we perform activity sensing in a scalable approach,it is essential make three key contributions in this paper.1)Instead of to transform the measurement of limb movements from the performing waveform matching on the inertial measurements watch coordinate system to the body coordinate system. in the raw data level,we extract the angle profiles to depict 1)From Watch Coordinate System to Global Coordinate the angle variation of limb movements,and leverage the System:Fig.2(a)shows the three axes of the watch coordinate meta-activity profiles to depict the complex activities in the system.The X-axis refers to the direction of the lower arm logical representation level,such that the derived recognition when the watch is worn on the wrist,the Yio-axis refers to the model is scalable enough for the activity recognition on any direction of the strap of the watch,and the 2-axis refers to arbitrary human subjects.2)We build a coordinate system the direction which is perpendicular to the watch surface. transformation scheme to transform the inertial measurement According to the acceleration measurements from the ac- from the watch coordinate system to the body coordinate celerometer,we can extract a constant gravitational accel- system,such that the limb movement can be depicted in a eration as a vector g from the low pass filter (such asThere are two key technical challenges in realizing the activity sensing scheme. The first challenge is to realize the activity sensing in a user-independent approach, such that the derived recognition model can be extended to recognize the activities of any arbitrary human subjects, regardless of the detailed differences and inherent deviations in the activities from different human subjects. To address this challenge, we propose to leverage the angle profiles, i.e., the angles between the specified limb and the coordinate axes, to depict the limb movements. The angle profiles are able to capture the angle variation of the limb movements relative to the human body, which tackle the deviation details caused by the user-specific characters like the height. Moreover, we propose the method of “meta-activity recognition” to perform activity sensing in the logical representation level, based on the sequence of meta￾activity profiles, so as to tackle the variations in the long se￾quence of small-range activities. Specifically, according to the inertial measurements collected from human motion, instead of performing the waveform-based matching like dynamic time warping, we decompose the complex activity into a sequence of meta-activities, and use this sequence to recognize the complex activity via the least edit distance-based matching. The second challenge is to build a consistent scheme to depict the human motion according to the inertial measure￾ments from the wearable devices. Since the human subjects may perform the activities towards any arbitrary direction during the human motion, this causes the templates for activity recognition to depend heavily on the actual direction the human body is facing, and further enhances the complexities in performing activity sensing due to the inconsistency. To address this challenge, we depict all the inertial measurements of human motion in terms of a body coordinate system in a consistent approach. Specifically, according to the gravity direction and the magnetic direction extracted from the inertial measurements, we transform the measurements from the watch coordinate system (WCS) to the global coordinate system (GCS). Then, by specifying two signal gestures, i.e., extending the arm to the front and dropping the arm downward, we can figure out the orientation of the human body in the global coordinate system according to the measurements in the signal gestures, thus we further transform the measurements to the body coordinate system (BCS). To the best of our knowledge, this paper presents the first study of using the method “meta-activity recognition” for logical cognition-based activity sensing. Specifically, we make three key contributions in this paper. 1) Instead of performing waveform matching on the inertial measurements in the raw data level, we extract the angle profiles to depict the angle variation of limb movements, and leverage the meta-activity profiles to depict the complex activities in the logical representation level, such that the derived recognition model is scalable enough for the activity recognition on any arbitrary human subjects. 2) We build a coordinate system transformation scheme to transform the inertial measurement from the watch coordinate system to the body coordinate system, such that the limb movement can be depicted in a consistent approach, regardless of the exact orientation of the human bodies. 3) We have implemented a prototype system to evaluate the real performance, the experiment results in real settings shows that our meta-activity recognition achieves an average accuracy of 92% for user-independent activity sensing. II. PROBLEM FORMULATION In this paper, we investigate the wearable approach for activity sensing, i.e., a wearable device is worn by the human subject to continuously collect the inertial measurements of human motion, then an activity sensing scheme is required to accurately recognize the complex activities of limb movements from human subjects. The complex activity refers to the kind of activity with a large range of movement, such as sit-ups and dumbbell lateral raise. Without loss of generality, we leverage the smart watch to sense the human motions, which is embedded with inertial sensors including the accelerometer, gyroscope and magnetometer. In this paper, we aim to design an activity sensing scheme, by considering the following metrics in system performance: 1) Accuracy: The expected accuracy for the activity sensing scheme to successfully match a specific activity to a correct activity should be greater than a specified threshold, e.g., 85%. 2) Time-efficiency: The time delay of the activity recognition process should be less than a specified threshold, e.g., 500ms. 3) User-independence: When performing activity sensing, no training data should be required from the specified user. 4) Lightweight-training: The essential quantity of the training samples to build the templates should be small enough. . III. MODELING THE HUMAN MOTION A. Coordinate System Transformation In regard to activity sensing, as the raw inertial measure￾ments are collected from the embedded inertial sensors in the smart watch, they are measured by reference to the body frame of the smart watch. However, the watch coordinate system is continuously changing with the arm/wrist movement during the process of human motion, thus the measurements from the watch coordinate system cannot be used as a stable reference for the specified activities. In fact, since the human subject may be performing the activity towards any arbitrary direction, the movements should be depicted as the movement of arms or legs relative to the human body, regardless of the absolute moving direction of the limbs. Therefore, in order to perform activity sensing in a scalable approach, it is essential to transform the measurement of limb movements from the watch coordinate system to the body coordinate system. 1) From Watch Coordinate System to Global Coordinate System: Fig. 2(a) shows the three axes of the watch coordinate system. The Xw-axis refers to the direction of the lower arm when the watch is worn on the wrist, the Yw-axis refers to the direction of the strap of the watch, and the Zw-axis refers to the direction which is perpendicular to the watch surface. According to the acceleration measurements from the ac￾celerometer, we can extract a constant gravitational accel￾eration as a vector g from the low pass filter (such as