Feature-First Add-On for Trajectory Simplification in Lifelog Applications

Abstract

Lifelog is a record of one’s personal experiences in daily lives. User’s location is one of the most common information for logging a human’s life. By understanding one’s spatial mobility we can figure out other pieces of context such as businesses and activities. With GPS technology we can collect accurate spatial and temporal details of a movement. However, most GPS receivers generate a huge amount of data making it difficult to process and store such data. In this paper, we develop a generic add-on algorithm, feature-first trajectory simplification, to simplify trajectory data in lifelog applications. It is based on a simple sliding window mechanism counting occurrence of certain conditions. By automatically identifying feature points such as signal lost and found, stall, and turn, the proposed scheme provides rich context more than spatio-temporal information of a trajectory. In experiments with a case study of commuting in personal vehicles, we evaluate the effectiveness of the scheme. We find the proposed scheme significantly enhances existing simplification algorithms preserving much richer context of a trajectory.

Empirical Approaches for Improving Inertial Pedestrian Navigation: Approximating Gravitational Acceleration and Digitizing Heading Change Angles

“…Also, the step detection counts 207 while the participant walks 208 steps, which results in detection accuracy of 99.52%. In order to quantitatively compare the effectiveness of the proposed ideas we use synchronized Euclidean distance (SED) metrics [2]. It can count on both spatial and temporal aspects of a trajectory.…”

“…Enhancements in mobile devices and wireless communication technology demand location-based services in various fields including aerospace, robotics, healthcare, wellness, and entertainment to provide information on the surroundings for user convenience. In outdoor environments, GNSS (Global Navigation Satellite System) handles the requirements [1], [2]. For example, GPS (Global Positioning System) satellites orbiting the earth transmit signals such that GPS receivers can determine their locations on the earth with those signals.…”

Review and classification of trajectory summarisation algorithms: From compression to segmentation

“…Multiple trajectory compression Similarity, 113 TrajStore, 151 Representativeness, 53 NaTS 144 Lossless trajectory compression PRESS, 25 COMPRESS, 96 CoTracks, 90 Trajic, 52 TrajStore, 151 IFC, 152 Lovell, 12,153 LWZ 154 Network road constrained PoI and PoIE, 61 Nonmaterial, 155 VTracer, 58 TSHL, 63 OGPC and OSPC, 47 Opheimimproved, 95 , RSLC and TSLC, 96 CFF, 64 MMTC-offline and MMTC-online, 48 FFDP and FFUS, 97 GS, 108 IC-MBR, 125 SUTC, 89 INCM, 118 STTrace and Thresholds, 75 ESTC-EDP, 119 STC, 22 BTC and HTC, 25 STMaker, 103,104 SNDSC, 122 CLEAN 57…”

“…PoI and PoIE, 61 TS, 127,128 2 stage-pls, 126 IMM, [35][36][37] OGPC and OSPC, 47 FFDP and FFUS, 97 S-DMin and SE-DMin, 129 ATS, 117 SMoT, 71 CB-SMoT, 21 Patroumpas, 73,74 STC, 22 RGRASP-SemTS, 24 BTC and HTC, 25 STMaker, 103,104 SELF, 121 SetraStream, 82 HESAVE and SNDSC, 122 SPD 84 then, using these, other algorithm is applied to select the measurements.…”

“…Distance SWS, 91 OWS, 92 WSII, 93 ISW, 94 Opheim-improved, 95 RSLC and TSLC, 96 FFUS, 97 FSW, 98 BQS, 68 ABQS, FBQS and PBQS, 69 FastSTray, 99 TD-TR, 3 SQUISH, 100 SQUISH-E(l) and SQUISH-E(m), 101 Opheim, 102 STMaker, 103,104 ISW-SPM, 105 TSA 1 and TSA 2,106 DPSW 107 Angle SWS, 91 OWS, 92 WSII, 93 ISW, 94 DPBGD, 86 FFUS, 97 GS, 108 BQS, 68 ABQS, FBQS and PBQS, 69 FastSTray, 99 62 Similarity, 113 DP, 114 SWS, 91 OWS, 92 WSII, 93 TCMM, 115 TD-TR reduce, 17 DPhull, 116 SWAB, 4 FFDP, 97 GRTSSec, 50 Pyramid, 19 ATS, 117 ADP, 20 INCM, 118 ESTC-EDP, 119 SPM, 120 STMaker, 103,104 ISW-SPM,…”