150 0 50 20 5° 10° 20° 30° 15 The number of human subjects in template construction (c)Accuracy for different number of training samples A1 总AAMA5帖A7随A1O AI2EAA5AGA78超A1O A1点9 A3 AA A5 AS A7A A A10 A100000051000000a00.00.00.0 A1U0000.i000a0a000a00000 At1.创Q0a0a0a0a0ai0ai0a000网 3000 200的00000.110000000.00.00.00.0 0.0100.00a80a00000m 30.0的0.0时0s000020000.030.00.B00.0 A3-0.600.601600.0Q600.00.00Q000000 A3a.00Q01.00.00000a00000a.000.00 A40050.010.000940000000.600.600.00.0 A4-0.00.600.01.60a.60a.600.00000.000.00- Ma.00Q.000001.0a.0Q0a00a0的a.000.00 2000 A50.0的0.0的00105098a000.00.00.00.0 A50.000.00010000的0060Q.0600000 A54.00意.000000.0100000000的0000.00 50.200500的0能00006的a00.00.50.07 A80.30.00.0a1208053aa0019012, 8.01000050500的a0的ai4024 A7040000000000001700.230.20.0 A70.00.00.300008002010a0mQ000m A7.00 .00.00.00 0.00 0.010.00 0.03 0.00 91000 80.000.000.000010000000.0的0.30.130.0 A80.00.00.00.a00.800.00001.m00000 h8a.00Q.0000000a0a0的1D的a.00000 A9a.1100的000ag0000i00.140.00.470.1 A9-0.0.0.200.200.的0.6005401Q0的0g0.0- 9a.5a.00.000.t0016112001a650.01- A10也140500的0a000236000.0010.42 1000014090990509900m0m29 The number of human subjects in templates (d)Confusion matrix for AM (e)Confusion matrix for APM (f)Confusion matrix for MAR (g)Time delay for different solutions Fig.10.The experiment results We first evaluate the performance in terms of recognition accuracy,as shown in Fig.10(c).It is found that,in almost accuracy when the angle6 is varied from5°to45°，as shown all situations,MAR achieves the best performance whereas AM in Fig.10(a).It is found that all the recognition accuracies are achieves the worst performance.Specifically,when the number greater than 83%when the angle is varied from 5 to 45. of human subjects in template construction is 1,the number The highest accuracy is achieved when the angle is set to 10, of training samples is small.AM achieves poor accuracy of whereas the lowest accuracy is achieved when the angle is 61%as it lacks enough templates for accurate matching. set to 45.The reason is that,when the angle is fairly large,APM leverages the angle profiles to mitigate the impact of e.g.,45,the granularity of the meta-activity is too coarse to variances in the raw inertial measurement,thus it enhances depict the movement of human subjects,thus it leads to many the accuracy from 61%to 81%.Due to the character of mismatches in activity recognition.However,when the angle logical cognition,MAR further improves the accuracy to 87% is fairly small,e.g.,5,the granularity of the meta-activity even if the training samples are so limited.This implies that is too fine to tolerate the detailed deviations due to human- our meta-activity recognition achieve rather good performance specific characters,thus the performance is also degraded in for user-independent recognition while requiring lightweight- comparison to the optimum case.Therefore,the parameter of training.As the number of templates increases,the accuracies the sector angle should be carefully selected for improving the of the three solutions are all increasing to a close value of performance in recognition accuracy. 92%.Nevertheless.MAR always achieves the least variances in We then evaluate the performance in terms of time efficiency recognition accuracy since it leads to very stable performance. when the angle6 is varied from5°to45°，as shown in Fig. 2)The matching ratios among multiple activities:We fur- 10(b).It is found that,as the angle 6 increases from 5 to ther investigate the confusion matrices for the three solutions, 30,the average time delay rapidly decreases from 145ms to as shown from Fig.10(d)to Fig.10(f).We set the number 21ms.The reduced time delay is caused by the increasing of human subjects in template construction to 2,and the granularity of the meta-activity,which reduces the processing activities are listed from A1 to A10 according to the order in time cost.However,when the angle o further increases to 45 Fig.1.According to the matching results in the three confusion the average time delay slightly increases to 49ms,as the time matrices,it is found that APM is able to reduce most of the delay for the meta-activity classification increases due to the mismatches caused by AM.so APM achieves the recognition increased size of input to the DTW algorithm.Therefore,to ratio of 100%for most activities.However,APM still fails to achieve an appropriate trade off between the accuracy and time accurately recognize some activities such as A6,Ag and A10. efficiency,in the following experiment,we set the angle o to since these activities usually have larger movement ranges 10 in MAR to achieve the optimized performance. and more movement variations in details.Fortunately,MAR is able to further reduce these mismatches and improve the C.Evaluate the Recognition Accuracy recognition accuracy to a fairly high level.Moreover,MAR 1)Sensitivity to the number of training samples:Since achieves the least variances in recognizing multiple activities we aim to achieve the lightweight-training recognition,we in comparison to the other two solutions. require the number of training samples to be sa small as possible.Therefore,as we collect 20 samples from each human D.Evaluate the Time Efficiency subject to build the templates for each complex activity,we We further evaluate the time delay of processing the activity vary the number of human subjects involved in the template sensing,respectively,for AM,APM and MAR.We vary the construction from I to 8,and evaluate the average recognition number of human subjects in template construction from I toThe angle of the meta-activity sector 5 ° 10 ° 20 ° 30 ° 45 ° Recognition accuracy 0.5 0.6 0.7 0.8 0.9 1 (a) Accuracy for different sector angles The angle of the meta-activity sector 5° 10° 20° 30° 45° Time delay(ms) 0 50 100 150 (b) Time delay for different sector angles The number of human subjects in template construction 1 2 4 8 Recognition Accuracy 0 0.2 0.4 0.6 0.8 1 AM APM MAR (c) Accuracy for different number of training samples 0.49 0.00 0.00 0.05 0.00 0.12 0.04 0.00 0.11 0.14 0.00 0.89 0.01 0.01 0.00 0.05 0.00 0.00 0.00 0.05 0.00 0.00 0.95 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.51 0.11 0.00 0.94 0.00 0.02 0.00 0.01 0.18 0.02 0.00 0.00 0.02 0.00 0.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.68 0.01 0.00 0.10 0.36 0.00 0.00 0.03 0.00 0.00 0.00 0.70 0.03 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.02 0.13 0.47 0.01 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.01 0.42 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 (d) Confusion matrix for AM 1.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 1.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.12 0.00 0.00 0.03 0.08 0.00 0.00 0.00 0.00 0.99 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.53 0.00 0.00 0.54 0.03 0.00 0.00 0.00 0.00 0.00 0.01 1.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.00 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.49 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 (e) Confusion matrix for APM 1.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.05 0.00 0.00 0.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.05 0.00 0.00 0.01 0.02 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.01 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.96 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.03 0.00 0.65 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.01 0.90 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 (f) Confusion matrix for MAR The number of human subjects in templates 1 2 4 8 Time delay(ms) 0 1000 2000 3000 AM APM MAR (g) Time delay for different solutions Fig. 10. The experiment results We first evaluate the performance in terms of recognition accuracy when the angle δ is varied from 5 ◦ to 45◦ , as shown in Fig. 10(a). It is found that all the recognition accuracies are greater than 83% when the angle is varied from 5 ◦ to 45◦ . The highest accuracy is achieved when the angle is set to 10◦ , whereas the lowest accuracy is achieved when the angle is set to 45◦ . The reason is that, when the angle is fairly large, e.g., 45◦ , the granularity of the meta-activity is too coarse to depict the movement of human subjects, thus it leads to many mismatches in activity recognition. However, when the angle is fairly small, e.g., 5 ◦ , the granularity of the meta-activity is too fine to tolerate the detailed deviations due to human￾specific characters, thus the performance is also degraded in comparison to the optimum case. Therefore, the parameter of the sector angle should be carefully selected for improving the performance in recognition accuracy. We then evaluate the performance in terms of time efficiency when the angle δ is varied from 5 ◦ to 45◦ , as shown in Fig. 10(b). It is found that, as the angle δ increases from 5 ◦ to 30◦ , the average time delay rapidly decreases from 145ms to 21ms. The reduced time delay is caused by the increasing granularity of the meta-activity, which reduces the processing time cost. However, when the angle δ further increases to 45◦ , the average time delay slightly increases to 49ms, as the time delay for the meta-activity classification increases due to the increased size of input to the DTW algorithm. Therefore, to achieve an appropriate trade off between the accuracy and time efficiency, in the following experiment, we set the angle δ to 10◦ in MAR to achieve the optimized performance. C. Evaluate the Recognition Accuracy 1) Sensitivity to the number of training samples: Since we aim to achieve the lightweight-training recognition, we require the number of training samples to be sa small as possible. Therefore, as we collect 20 samples from each human subject to build the templates for each complex activity, we vary the number of human subjects involved in the template construction from 1 to 8, and evaluate the average recognition accuracy, as shown in Fig. 10(c). It is found that, in almost all situations, MAR achieves the best performance whereas AM achieves the worst performance. Specifically, when the number of human subjects in template construction is 1, the number of training samples is small. AM achieves poor accuracy of 61% as it lacks enough templates for accurate matching. APM leverages the angle profiles to mitigate the impact of variances in the raw inertial measurement, thus it enhances the accuracy from 61% to 81%. Due to the character of logical cognition, MAR further improves the accuracy to 87% even if the training samples are so limited. This implies that our meta-activity recognition achieve rather good performance for user-independent recognition while requiring lightweight￾training. As the number of templates increases, the accuracies of the three solutions are all increasing to a close value of 92%. Nevertheless, MAR always achieves the least variances in recognition accuracy since it leads to very stable performance. 2) The matching ratios among multiple activities: We fur￾ther investigate the confusion matrices for the three solutions, as shown from Fig.10(d) to Fig.10(f). We set the number of human subjects in template construction to 2, and the activities are listed from A1 to A10 according to the order in Fig.1. According to the matching results in the three confusion matrices, it is found that APM is able to reduce most of the mismatches caused by AM, so APM achieves the recognition ratio of 100% for most activities. However, APM still fails to accurately recognize some activities such as A6, A9 and A10, since these activities usually have larger movement ranges and more movement variations in details. Fortunately, MAR is able to further reduce these mismatches and improve the recognition accuracy to a fairly high level. Moreover, MAR achieves the least variances in recognizing multiple activities in comparison to the other two solutions. D. Evaluate the Time Efficiency We further evaluate the time delay of processing the activity sensing, respectively, for AM, APM and MAR. We vary the number of human subjects in template construction from 1 to