2148 IEEE/ACM TRANSACTIONS ON NETWORKING,VOL.26.NO.5.OCTOBER 2018 orthogonal and parallel directions of the gravity,respectively. We then use this model to calibrate the gyroscope-based estimation with the Minimum Mean Square Error (MMSE) estimator. Smant Phone:斜Sumoung3 D.Summary of Experimental Results We implemented our MOSS system on mobile devices including Google Glass and Samsung S5 smart phones.They Fig.2.Measurement setup are deployed at different body locations of each human subject. We let the human subjects walk along different types of traces angles of a human subject so that the indoor pathway maps in outdoor environments with different moving modes,i.e., can be obtained [21].Wang et al.[22]used compasss,gyro- walk,run,and jump.We use two main metrics to evaluate scopes,and WiFi landmarks to estimate the absolute walking the performance:(1)angle accuracy:the angle deviation direction of a human subject.Of prior work in this category, between the estimated human body movement direction and only the APT system does not use magnetometer sensors [24]; the ground truth,and (2)coordinate accuracy:the similarity instead,APT uses accelerometer and gyroscope sensors to between the synchronized coordinate and the ground truth. obtain the walking direction of a human subject.Compared Experiment results show that MOSS achieves an average angle with our work,both the WalkCompass and APT does not accuracy of 9.8 and an average coordinate accuracy of 97%. address space synchronization among multiple mobile devices A real-world case study with free activities further shows that as they use only one smartphone.In contrast,in this paper MOSS achieves an average angle accuracy of 12 and an we investigate multiple mobile devices instead of one device average coordinate accuracy of 91%. to synchronize the inertial readings from multiple devices in spatial dimension.We propose a more generalized solution II.RELATED WORK to heading direction estimation for multiple wearable devices Orientation Estimation:Much work has been done on subject to different but correlated accelerations.Our solution estimating the orientation of a mobile device (such as a neither relies on the inaccurate magnetometer measurements smartphone)using accelerometer,gyroscope,and magne- nor uses any application specific features tometer sensors [2],[6].[9]-[17].Compared with our work, most of such work uses magnetometers.For example III.UNDERSTANDING HUMAN MOTIONS Madgwick et al.[13]proposed a quaternion representation to incorporate accelerometer and magnetometer readings for A.Measurements orientation estimation.Zhou et al.[6]used the accelerometer Measurement Setup:We placed six mobile devices,one and magnetometer to assist gyroscope in orientation estimation smart glass(Google Glass 2)and five smartphones(Samsung by selecting the best sensing capabilities.Gowda et al.[14] Galaxy S5).at different body location of a human subject tried to map from a local frame of the sensor to a global frame to continuously collect the inertial measurements in his daily of reference,so as to track a ball's 3D trajectory and spin life.As shown in Fig.2,the Google Glass was placed on with inertial sensors and radios embedded in the ball.Of prior the head and the five phones were placed at five different work in this category,only two systems,Autowitness [18]locations on the body.These devices are all equipped with an and Nericell [19],which finds the rotation matrix between the accelerometer,a gyroscope,and a magnetometer.We use the local coordinate of a smartphone and the reference coordinate three axes obtained from the magnetometers as the reference by the acceleration measurement,did not use magnetometers. global coordinate. They used a vehicle's forwarding accelerations (i.e.,speeding Measurement of Human Body Movements:We observed that up and slowing down)obtained from accelerometer readings. when the human subject attached with multiple mobile devices Compared with our work,they have two key limitations as moves forward,the inconsistent accelerations from intra-body they attach mobile devices to moving vehicles rather than movement have different directions and magnitude.To extract human subjects.First,as there is no intra-body movements the consistent and the inconsistent accelerations,we let the and a vehicle moves much straighter than human moves,they human subject walk along a straight path for 30 seconds.We do not need to deal with our first technical challenge.Second, attached an additional IMU sensor on the chest to estimate the as the mobile devices do not move after a vehicle stops,they ground-truth of the consistent acceleration as the inconsistent do not need to deal with our second technical challenge. accelerations on the chest is negligible.We collected the Direction Estimation:Recently some work explored the acceleration measurements from the six devices as mixed estimation of heading directions for dead reckoning-based accelerations and then extract the inconsistent accelerations navigation schemes [20]-[26].Compared with our work,most from each device by subtracting the consistent acceleration. of such work uses magnetometers to estimate the heading To illustrate the direction and magnitude of both the consistent directions while trying to mitigate the magnetic interference and inconsistent accelerations,we plot them as vectors in the from indoor environments.For example,WalkCompass used polar coordinate system corresponding to the earth coordinate magnetometers to estimate the walking direction of a human system.We plot a vector of the inconsistent acceleration subject [20.Walkie-Markie used the magnetometer and gyro- for every 100 ms during the time interval of 5 seconds. scopes in smartphones to get the walking direction and turning Fig.3 shows the directions and magnitudes of the original2148 IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 26, NO. 5, OCTOBER 2018 orthogonal and parallel directions of the gravity, respectively. We then use this model to calibrate the gyroscope-based estimation with the Minimum Mean Square Error (MMSE) estimator. D. Summary of Experimental Results We implemented our MOSS system on mobile devices including Google Glass and Samsung S5 smart phones. They are deployed at different body locations of each human subject. We let the human subjects walk along different types of traces in outdoor environments with different moving modes, i.e., walk, run, and jump. We use two main metrics to evaluate the performance: (1) angle accuracy: the angle deviation between the estimated human body movement direction and the ground truth, and (2) coordinate accuracy: the similarity between the synchronized coordinate and the ground truth. Experiment results show that MOSS achieves an average angle accuracy of 9.8◦ and an average coordinate accuracy of 97%. A real-world case study with free activities further shows that MOSS achieves an average angle accuracy of 12◦ and an average coordinate accuracy of 91%. II. RELATED WORK Orientation Estimation: Much work has been done on estimating the orientation of a mobile device (such as a smartphone) using accelerometer, gyroscope, and magne￾tometer sensors [2], [6], [9]–[17]. Compared with our work, most of such work uses magnetometers. For example, Madgwick et al. [13] proposed a quaternion representation to incorporate accelerometer and magnetometer readings for orientation estimation . Zhou et al. [6] used the accelerometer and magnetometer to assist gyroscope in orientation estimation by selecting the best sensing capabilities. Gowda et al. [14] tried to map from a local frame of the sensor to a global frame of reference, so as to track a ball’s 3D trajectory and spin with inertial sensors and radios embedded in the ball. Of prior work in this category, only two systems, Autowitness [18] and Nericell [19], which finds the rotation matrix between the local coordinate of a smartphone and the reference coordinate by the acceleration measurement, did not use magnetometers. They used a vehicle’s forwarding accelerations (i.e., speeding up and slowing down) obtained from accelerometer readings. Compared with our work, they have two key limitations as they attach mobile devices to moving vehicles rather than human subjects. First, as there is no intra-body movements and a vehicle moves much straighter than human moves, they do not need to deal with our first technical challenge. Second, as the mobile devices do not move after a vehicle stops, they do not need to deal with our second technical challenge. Direction Estimation: Recently some work explored the estimation of heading directions for dead reckoning-based navigation schemes [20]–[26]. Compared with our work, most of such work uses magnetometers to estimate the heading directions while trying to mitigate the magnetic interference from indoor environments. For example, WalkCompass used magnetometers to estimate the walking direction of a human subject [20]. Walkie-Markie used the magnetometer and gyro￾scopes in smartphones to get the walking direction and turning Fig. 2. Measurement setup. angles of a human subject so that the indoor pathway maps can be obtained [21]. Wang et al. [22] used compasss, gyro￾scopes, and WiFi landmarks to estimate the absolute walking direction of a human subject. Of prior work in this category, only the APT system does not use magnetometer sensors [24]; instead, APT uses accelerometer and gyroscope sensors to obtain the walking direction of a human subject. Compared with our work, both the WalkCompass and APT does not address space synchronization among multiple mobile devices as they use only one smartphone. In contrast, in this paper we investigate multiple mobile devices instead of one device to synchronize the inertial readings from multiple devices in spatial dimension. We propose a more generalized solution to heading direction estimation for multiple wearable devices subject to different but correlated accelerations. Our solution neither relies on the inaccurate magnetometer measurements nor uses any application specific features. III. UNDERSTANDING HUMAN MOTIONS A. Measurements Measurement Setup: We placed six mobile devices, one smart glass (Google Glass 2) and five smartphones (Samsung Galaxy S5), at different body location of a human subject to continuously collect the inertial measurements in his daily life. As shown in Fig. 2, the Google Glass was placed on the head and the five phones were placed at five different locations on the body. These devices are all equipped with an accelerometer, a gyroscope, and a magnetometer. We use the three axes obtained from the magnetometers as the reference global coordinate. Measurement of Human Body Movements: We observed that when the human subject attached with multiple mobile devices moves forward, the inconsistent accelerations from intra-body movement have different directions and magnitude. To extract the consistent and the inconsistent accelerations, we let the human subject walk along a straight path for 30 seconds. We attached an additional IMU sensor on the chest to estimate the ground-truth of the consistent acceleration as the inconsistent accelerations on the chest is negligible. We collected the acceleration measurements from the six devices as mixed accelerations and then extract the inconsistent accelerations from each device by subtracting the consistent acceleration. To illustrate the direction and magnitude of both the consistent and inconsistent accelerations, we plot them as vectors in the polar coordinate system corresponding to the earth coordinate system. We plot a vector of the inconsistent acceleration for every 100 ms during the time interval of 5 seconds. Fig. 3 shows the directions and magnitudes of the original