8 and evaluate the corresponding time delay.It is found that,Our solution extracts the angle profiles to depict the angle in all situations,MAR achieves much smaller time delay (all variation of limb movement in the consistent body coordinate less than 45ms)than AM and APM.Moreover,as the number system.It further extracts the meta-activity profiles to depict of human subjects in template construction increases from 1 to the sequence of small range activities in the complex activity. 8,the time delay of AM and APM increases rapidly,whereas By leveraging the least edit distance-based matching scheme, the time delay of MAR keeps fairly stable.The reason is as the experiment results shows that our solution achieves an follows:As both AM and APM uses the DTW for matching,it average accuracy of 92%for user-independent activity sensing. requires a large amount of time to process the measurements ACKNOWLEDGMENTS with a small granularity in the raw data level,however,MAR This work is supported in part by National Natural Science processes the measurements with a much larger granularity in Foundation of China under Grant Nos.61472185,61373129, the meta-activity level.It takes most of the processing time 61321491,61502224;JiangSu Natural Science Foundation, on the meta-activity segmentation rather than matching.Thus, No.BK20151390.This work is partially supported by Col- MAR achieves the best time efficiency in tens of milliseconds. laborative Innovation Center of Novel Software Technology VI.RELATED WORK and Industrialization REFERENCES Wearable Device.Recent researches consider leveraging the inertial sensors embedded in wearable devices to detect [1]A.Parate.M.C.Chiu.C.Chadowitz.D.Ganesan.and E.Kalogerakis and monitor the user's activities [1],[8]-[13].Wrist mounted Risq:Recognizing smoking gestures with inertial sensors on a wristband In Proceedings of ACM MobiSys.2014. inertial sensors are widely used for arm-based activity sensing [2]C.Karatas,L.Liu,H.Li,J.Liu,Y.Wang,S.Tan,J.Yang,Y.Chen [1].[2].RistQ [1]leverages the accelerations from a wrist M.Gruteser,and R.Martin.Leveraging wearables for steering and driver tracking.In Proceedings of IEEE INFOCOM,2016. strap to detect and recognize smoking gestures.Karatas et al. [3]S.Jain,C.Borgiattino,Y.Ren,M.Gruteser,Y.Chen,and C.Fabiana [2]uses wrist mounted inertial sensors to track steering wheel Chiasserini.Lookup:Enabling pedestrian safety services via shoe usage and angle.Foot-mounted inertial sensors are leveraged sensing.In Proceedings of ACM MobiSys,2015. for indoor localization by sensing the patterns of footsteps [4]S.Butterworth.On the theory of filter amplifiers.Wireless Engineer, 7(6:536-541.1930. [12],[13].LookUp [3]uses shoe-mounted inertial sensors for [5]M.R.Spiegel,S.Lipschutz,and D.Spellman.Vector analysis.Schatmns location classification based on surface gradient profile and Outlines(2nd ed.).pages 15-25.2009. step patterns.Robertson et al.[12]proposes an approach for [6]K.Shoemake.Animating rotation with quaternion curves.Computer Graphics,19(3):245-254.1985. simultaneous mapping and localization for pedestrians based [7]G.Navarro.A guided tour to approximate string matching.ACM on odometry with foot mounted inertial sensors. Computing Surveys,33(11):31-88,2001. [8]S.Consolvo,D.W.McDonald,T.Toscos.M.Y.Chen,J.Froehlich. Wireless Signals.Another branch of activity recognition B.Harrison,P.Klasnja.A.LaMarca,L.LeGrand,and R.Libby.Activity solutions exploit the change of wireless signals (including sensing in the wild:A field trial of ubifit garden.In Proceedings of ACM WiFi signals,RF-signals,etc.)incurred by the human activities CHL.2008. [9]K.-H.Chang.M.Y.Chen,and J.Canny.Tracking free-weight exercises. [14]-[19].FEMO [14]provides a free-weight exercise moni- In Proceedings of ACM UbiComp,2007 toring scheme by attaching RFID tags on the dumbbells and [10]A.Khany,S.Mellory,E.BerlinN,R.Thompsony,R.McNaneyy, leveraging the Doppler shift profile of the reflected backscatter P.Oliviery,and T.Plotzy.Beyond activity recognition:Skill assessment from accelerometer data.In Proceedings of ACM UbiComp,2015. signals for activity recognition.Wang et al.[15]propose a [11]N.Roy,H.Wang,and R.R.Choudhury.I am a smartphone and I can CSI based human activity recognition and monitoring system, tell my user's walking direction.In Proceedings of MobiSys,2014. by quantitatively building the correlation between CSI value [12]P.Robertson.M.Angermann,and B.Krach.Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors. dynamics and a specific human activity.E-eyes [16]presents In Proceedings of ACM UbiComp,2009 device-free location-oriented activity identification at home [13]O.Woodman and R.Harle.Pedestrian localization for indoor environ- through the use of fine-grained WiFi signatures.RF-IDraw ments.In Proceedings of ACM UbiComp.2008. [17]can infer a humans writing by tracking a passive RFID [14]H.Ding.L.Shangguan,Z.Yang.J.Han,Z.Zhou,P.Yang.W.Xi,and J.Zhao.Femo:A platform for free-weight exercise monitoring with tag attached to his/her fingers. rfids.In Proceedings of ACM SenSys,2015. However,most of the above activity recognition schemes [15]W.Wang.A.X.Liu,M.Shahzad,K.Ling,and S.Lu.Understanding and modeling of wifi signal based human activity recognition.In leverage the traditional waveform-based matching to process Proceedings of ACM MobiCom,2015. the inertial measurement/wireless signals in the raw data [16]Y.Wang.J.Liu.Y.Chen.M.Gruteser,J.Yang,and H.Liu.E-eyes: level.In this paper,we propose the meta-activity recogni- device-free location-oriented activity identification using fine-grained tion,which belongs to logic cognition-based activity sensing. wifi signatures.In Proceedings of ACM MOBICOM,2014. [17]J.Wang.D.Vasisht,and D.Katabi.RF-IDraw:Virtual touch screen in Our approach achieves lightweight-training recognition,which the air using RF signals.In Proc.of ACM SIGCOMM,2014. requires a small quantity of training samples to build the [18]X.Zheng,J.Wang.L.Shangguan.Z.Zhou,and Y.Liu.Smokey: Ubiquitous smoking detection with commercial wifi infrastructures.In templates,and user-independent recognition,which requires Proceedings of IEEE INFOCOM,2016. no training from the specific user. [19]Q.Pu,S.Gupta.S.Gollakota,and S.Patel.Whole-home gesture VII.CONCLUSION recognition using wireless signals.In Proceedings of ACM MOBICOM. 2013. In this paper,we propose a wearable approach for logic cognition-based activity sensing scheme in the logical repre- sentation level,by leveraging the meta-activity recognition.8 and evaluate the corresponding time delay. It is found that, in all situations, MAR achieves much smaller time delay (all less than 45ms) than AM and APM. Moreover, as the number of human subjects in template construction increases from 1 to 8, the time delay of AM and APM increases rapidly, whereas the time delay of MAR keeps fairly stable. The reason is as follows: As both AM and APM uses the DTW for matching, it requires a large amount of time to process the measurements with a small granularity in the raw data level, however, MAR processes the measurements with a much larger granularity in the meta-activity level. It takes most of the processing time on the meta-activity segmentation rather than matching. Thus, MAR achieves the best time efficiency in tens of milliseconds. VI. RELATED WORK Wearable Device. Recent researches consider leveraging the inertial sensors embedded in wearable devices to detect and monitor the user’s activities [1], [8]–[13]. Wrist mounted inertial sensors are widely used for arm-based activity sensing [1], [2]. RistQ [1] leverages the accelerations from a wrist strap to detect and recognize smoking gestures. Karatas et al. [2] uses wrist mounted inertial sensors to track steering wheel usage and angle. Foot-mounted inertial sensors are leveraged for indoor localization by sensing the patterns of footsteps [12], [13]. LookUp [3] uses shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. Robertson et al. [12] proposes an approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. Wireless Signals. Another branch of activity recognition solutions exploit the change of wireless signals (including WiFi signals, RF-signals, etc.) incurred by the human activities [14]–[19]. FEMO [14] provides a free-weight exercise moni￾toring scheme by attaching RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for activity recognition. Wang et al. [15] propose a CSI based human activity recognition and monitoring system, by quantitatively building the correlation between CSI value dynamics and a specific human activity. E-eyes [16] presents device-free location-oriented activity identification at home through the use of fine-grained WiFi signatures. RF-IDraw [17] can infer a humans writing by tracking a passive RFID tag attached to his/her fingers. However, most of the above activity recognition schemes leverage the traditional waveform-based matching to process the inertial measurement/wireless signals in the raw data level. In this paper, we propose the meta-activity recogni￾tion, which belongs to logic cognition-based activity sensing. Our approach achieves lightweight-training recognition, which requires a small quantity of training samples to build the templates, and user-independent recognition, which requires no training from the specific user. VII. CONCLUSION In this paper, we propose a wearable approach for logic cognition-based activity sensing scheme in the logical repre￾sentation level, by leveraging the meta-activity recognition. Our solution extracts the angle profiles to depict the angle variation of limb movement in the consistent body coordinate system. It further extracts the meta-activity profiles to depict the sequence of small range activities in the complex activity. By leveraging the least edit distance-based matching scheme, the experiment results shows that our solution achieves an average accuracy of 92% for user-independent activity sensing. ACKNOWLEDGMENTS This work is supported in part by National Natural Science Foundation of China under Grant Nos. 61472185, 61373129, 61321491, 61502224; JiangSu Natural Science Foundation, No. BK20151390. This work is partially supported by Col￾laborative Innovation Center of Novel Software Technology and Industrialization. REFERENCES [1] A. Parate, M. C. Chiu, C. Chadowitz, D. Ganesan, and E. Kalogerakis. Risq: Recognizing smoking gestures with inertial sensors on a wristband. In Proceedings of ACM MobiSys, 2014. [2] C. Karatas, L. Liu, H. Li, J. Liu, Y. Wang, S. Tan, J. Yang, Y. Chen, M. Gruteser, and R. Martin. Leveraging wearables for steering and driver tracking. In Proceedings of IEEE INFOCOM, 2016. [3] S. Jain, C. Borgiattino, Y. Ren, M. Gruteser, Y. Chen, and C. Fabiana Chiasserini. Lookup: Enabling pedestrian safety services via shoe sensing. In Proceedings of ACM MobiSys, 2015. [4] S. Butterworth. On the theory of filter amplifiers. Wireless Engineer, 7(6):536–541, 1930. [5] M. R. Spiegel, S. Lipschutz, and D. Spellman. Vector analysis. Schaums Outlines (2nd ed.), pages 15–25, 2009. [6] K. Shoemake. Animating rotation with quaternion curves. Computer Graphics, 19(3):245–254, 1985. [7] G. Navarro. A guided tour to approximate string matching. ACM Computing Surveys, 33(11):31–88, 2001. [8] S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, and R. Libby. Activity sensing in the wild: A field trial of ubifit garden. In Proceedings of ACM CHI, 2008. [9] K.-H. Chang, M. Y. Chen, and J. Canny. Tracking free-weight exercises. In Proceedings of ACM UbiComp, 2007. [10] A. Khany, S. Mellory, E. BerlinN, R. Thompsony, R. McNaneyy, P. Oliviery, and T. Plotzy. Beyond activity recognition: Skill assessment from accelerometer data. In Proceedings of ACM UbiComp, 2015. [11] N. Roy, H. Wang, and R. R. Choudhury. I am a smartphone and I can tell my user’s walking direction. In Proceedings of MobiSys, 2014. [12] P. Robertson, M. Angermann, and B. Krach. Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors. In Proceedings of ACM UbiComp, 2009. [13] O. Woodman and R. Harle. Pedestrian localization for indoor environ￾ments. In Proceedings of ACM UbiComp, 2008. [14] H. Ding, L. Shangguan, Z. Yang, J. Han, Z. Zhou, P. Yang, W. Xi, and J. Zhao. Femo: A platform for free-weight exercise monitoring with rfids. In Proceedings of ACM SenSys, 2015. [15] W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu. Understanding and modeling of wifi signal based human activity recognition. In Proceedings of ACM MobiCom, 2015. [16] Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu. E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In Proceedings of ACM MOBICOM, 2014. [17] J. Wang, D. Vasisht, and D. Katabi. RF-IDraw: Virtual touch screen in the air using RF signals. In Proc. of ACM SIGCOMM, 2014. [18] X. Zheng, J. Wang, L. Shangguan, Z. Zhou, and Y. Liu. Smokey: Ubiquitous smoking detection with commercial wifi infrastructures. In Proceedings of IEEE INFOCOM, 2016. [19] Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home gesture recognition using wireless signals. In Proceedings of ACM MOBICOM, 2013