6533b7d5fe1ef96bd12647ec
RESEARCH PRODUCT
A Trajectory-Driven 3D Channel Model for Human Activity Recognition
Ahmed AbdelgawwadAndreu Catala MallofreMatthias Patzoldsubject
Channel transfer functionGeneral Computer ScienceComputer scienceInternet of ThingsComputació centrada en humansDoppler EfecteActivity recognitionsymbols.namesakespectrogramInertial measurement unitVDP::Teknologi: 500::Medisinsk teknologi: 620Channel measurementsnon-stationary fading channelsmedicineGeneral Materials ScienceComputer visionNon-stationary fading channelsReconeixement de formes (Informàtica)channel state informationSpectrogrambusiness.industryWireless sensingGeneral Engineering:Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes [Àrees temàtiques de la UPC]TorsoInertial measurement unitsExpression (mathematics)TK1-9971Time-variant Doppler power characteristicsmedicine.anatomical_structureChannel state informationHuman-centered computingchannel transfer functiontime-variant Doppler power characteristicsTrajectorysymbolsChannel state informationSpectrogramMicro-Doppler signature:Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC]Electrical engineering. Electronics. Nuclear engineeringArtificial intelligenceHuman activity recognitionbusinessDoppler effectdescription
This paper concerns the design, analysis, and simulation of a 3D non-stationary channel model fed with inertial measurement unit (IMU) data. The work in this paper provides a framework for simulating the micro-Doppler signatures of indoor channels for human activity recognition by using radiofrequency-based sensing technologies. The major human body segments, such as wrists, ankles, torso, and head, are modelled as a cluster of moving point scatterers. We provide expressions for the time variant (TV) speed and TV angles of motion based on 3D trajectories of the moving person. Moreover, we present mathematical expressions for the TV Doppler shifts and TV path gains associated with each moving point scatterer. Furthermore, a model of the non-stationary time variant channel transfer function (TV-CTF) is provided, which takes into account the effects caused by a moving person as well as fixed objects, such as furniture, walls, and ceiling. The micro-Doppler signatures of the moving person is extracted from the TV-CTF by employing the concept of the spectrogram, whose expression is also provided in closed form. Our model is confirmed by channel state information (CSI) measurements taken during walking, falling, and sitting activities. The proposed channel model is fed with IMU data that has been collected. We evaluate the micro-Doppler signature of the model and CSI measurements. The results show a good agreement between the spectrograms and the TV mean Doppler shifts of our IMU-driven channel model and the measured CSI. The proposed model enables a paradigm shift from traditional experimental-based approaches to future simulation-based approaches for the design of human activity recognition systems. This work was supported in part by the WiCare Project under Grant 261895/F20, and in part by the Research Council of Norway (RCN). Peer Reviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-28 |