"... In PAGE 7: ...Batty and Miller, 2000, p. 138). After re-visiting the three types of constraints for human activities, we can see that capability constraints and authority constraints still apply to individuals in determining ifthey can conduct activities in virtual space; coupling constraints can control if individuals are able to interact with each other through tele-presence. Table2 sums up the contents of the three types of constraints for physical and virtual activities. The three types of constraints work together to determine which activities in physical and virtual spaces can be carried out by individuals.... ..."

"... In PAGE 5: ...ox 7: Forth Estuary, Scotland, U.K. 58 Box 8: Ria de Aveiro, Portugal 61 Box 9 The example of North Carolina 61 Box 10: EIA Objectives 63 List of Tables Table 1. Environmental parameters to characterise natural functions 12 Table2 : Pressures on the natural system due to human activities 33 Table 3: Tools of ICARM 55 Table 4: Functions of Nature... ..."

"... In PAGE 17: ... We used the method described in Section 4 to the trajectories of these points to compute the DI for each of these sequences. These values are shown in Table1 for various activities. Please note that the DI is used to estimate the number of basis... In PAGE 18: ...shapes needed for 3D deformable object modeling, not for activity recognition. From Table1 , a number of interesting observations can be made. For the walk sequences, the DI is between 5 and 6.... In PAGE 19: ... 4(b). The row and column numbers correspond to the numbers in Table1 for 1-16, while 17 and 18 correspond to sitting and walking, where the training and test data are from two difierent viewing directions. For the moment, consider the upper 13 x 13 block of this matrix.... In PAGE 20: ... (b): The similarity matrix for the various activities, including ones with difierent viewing directions. The numbers correspond to the numbers in Table1 for 1-16. 17 and 18 correspond to sitting and walking, where the training and test data are from two difierent viewing directions.... In PAGE 20: ... In order to further show the efiectiveness of this approach, we used the ob- tained similarity matrix to analyze the recognition rates for difierent clusters of activities, We applied difierent thresholds on the matrix and calculated the recall and precision values for each cluster. The flrst cluster contains the walking sequences along with jogging and blind walk (activities 1-5,11, and 12 in Table1 ).... In PAGE 20: ...2 in Table 1). Fig. 5(a) shows the recall vs. precision values for this activity cluster, we can see from the flgure that we are able to identify 90% of these activities with a precision up to 90%. The second cluster consists of three sit- ting sequences (activities 6-8 in Table1 ), and the third cluster consists of the brooming sequences (activities 9 and 10 in Table 1). For both of these clusters the similarity values were quite separated to the extent that we were able to fully separate the positive and negative examples.... In PAGE 20: ...2 in Table 1). Fig. 5(a) shows the recall vs. precision values for this activity cluster, we can see from the flgure that we are able to identify 90% of these activities with a precision up to 90%. The second cluster consists of three sit- ting sequences (activities 6-8 in Table 1), and the third cluster consists of the brooming sequences (activities 9 and 10 in Table1 ). For both of these clusters the similarity values were quite separated to the extent that we were able to fully separate the positive and negative examples.... In PAGE 21: ...ig. 5. The recall vs. precision rates for the detection of three difierent clusters of activities. (a) Walking activities (activities 1-5,11, and 12 in Table1 ) (b) Sitting activities (activities 6-9 in Table 1 ) (c) Brooming activities (activities 9 and 10 in Table 1) image plane, two for jogging in a circle and one for brooming in a circle. We considered a portion of these sequences where the stick flgure is not parallel to the camera.... In PAGE 21: ...ig. 5. The recall vs. precision rates for the detection of three difierent clusters of activities. (a) Walking activities (activities 1-5,11, and 12 in Table 1) (b) Sitting activities (activities 6-9 in Table1 ) (c) Brooming activities (activities 9 and 10 in Table 1) image plane, two for jogging in a circle and one for brooming in a circle. We considered a portion of these sequences where the stick flgure is not parallel to the camera.... ..."

"... In PAGE 5: ... In all the cases, the error at none of the feature points was more than 1 pixel. From Table1 , a number of interesting observations can be made. For the walk sequences, the deformability index was between 5 and 6.... ..."

"... In PAGE 5: ...) have been on the market for years that operate in this mode and researchers are driven to leverage other devices into this niche, while finding alternative ways to tap excess energy from human activity [90, 43]. Table1 provides a perspective on the amount of power used by the human body during various activities. Everyday human activity consumes power at a rate of 81-1630W, a factor of 20 in energy use.... In PAGE 6: ... For the sake of discussion, the theoretical Carnot limit will be used in the analysis below, hence the numbers are optimistic. Table1 indicates that for sitting, a total of 116 W of power is available. Using a Carnot engine to model the recoverable energy yields 3.... In PAGE 10: ... 10 Hand Waving: Power from Arm Motion While finger motion might allow for powering buttons or keyboards, intentional arm motion might generate enough power for notebook computing. The comparison of the activities listed in Table1 indicates that violin playing and housekeeping use up to 30 kcal/hr, or BFBCCZCRCPD0 BDCWD6 B4 BGBMBDBLC2 BDCRCPD0D3D6CXCT B5B4 BDCWD6 BFBN BIBCBCD7CTCR B5BPBFBHCF... ..."