Matthias Uwe Pätzold

• Zhakatayev, Altay; Avazov, Nurilla; Najjar, Hasan; Rogovchenko, Yuriy & Pätzold, Matthias Uwe (2024). Blind parameter identification of implicit differential equations using the collocation discretization and homotopy optimization methods. Mechanism and Machine Theory. ISSN 0094-114X. 200. doi: 10.1016/j.mechmachtheory.2024.105715.
• Hajri, Nazih; Youssef, Neji; Pätzold, Matthias Uwe & Mathiopoulos, P. Takis (2024). Performance Analysis of Diversity Reception over Double-Hoyt Fading Channels. IEEE Access. ISSN 2169-3536. doi: 10.1109/ACCESS.2024.3418872.
• Waqar, Sahil; Muaaz, Muhammad; Sigg, Stephan & Pätzold, Matthias Uwe (2024). A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis. IEEE Sensors Journal. ISSN 1530-437X. 24(10), p. 16614–16628. doi: 10.1109/JSEN.2024.3386221.
• Waqar, Sahil & Pätzold, Matthias Uwe (2023). A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors. IEEE Internet of Things Journal. ISSN 2327-4662. 11(8), p. 14494–14507. doi: 10.1109/JIOT.2023.3344179.
• Waqar, Sahil; Muaaz, Muhammad & Pätzold, Matthias Uwe (2023). Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning. IEEE Sensors Journal. ISSN 1530-437X. 23(20), p. 24916–24929. doi: 10.1109/JSEN.2023.3310620. Full text in Research Archive Show summary

  Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either towards or away from the radar. The monostatic single-input single-output (SISO) and monostatic multiple-input multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar’s boresight axis. Due to this physical layer limitation, today’s radar-based HAR systems fail to classify multi-directional human activities. In this paper, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multi-directional human movement from two perspectives, which allows to compute two distinct time-variant radial velocity distributions. A feature extraction network extracts numerous features from the measured time-variant radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multi-perspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems.

• Muaaz, Muhammad; Waqar, Sahil & Pätzold, Matthias Uwe (2023). Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing. Sensors. ISSN 1424-8220. 23(13). doi: 10.3390/s23135810. Full text in Research Archive Show summary

  RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.

• Zhakatayev, Altay; Rogovchenko, Yuriy & Pätzold, Matthias Uwe (2023). Recursive inverse dynamics sensitivity analysis of open-tree-type multibody systems. Nonlinear dynamics. ISSN 0924-090X. 111, p. 11297–11313. doi: 10.1007/s11071-023-08433-7. Show summary

  We present a first-order recursive approach to sensitivity analysis based on the application of the direct differentiation method to the inverse Lagrangian dynamics of rigid multibody systems. Our method is simple and efficient and is characterized by the following features. Firstly, it describes the kinematics of multibody systems using branch connectivity graphs and joint-branch connectivity matrices. For most mechanical systems with an open-tree kinematic structure, this method turns out to be more efficient compared to other kinematic descriptions employing joint or link connectivity graphs. Secondly, a recursive sensitivity analysis is presented for a dynamic system with an open-tree kinematic structure and inverse dynamic equations described in terms of the Lagrangian formalism. Thirdly, known approaches to recursive inverse dynamic and sensitivity analyses are modified to include dynamic systems with external forces and torques acting simultaneously at all joints. Finally, the proposed method for sensitivity analysis is easy to implement and computationally efficient. It can be utilized to evaluate the derivatives of the dynamic equations of multibody systems in gradient-based optimization algorithms. It also allows less experienced users to perform sensitivity analyses using the power of high-level programming languages such as MATLAB. To illustrate the method, simulation results for a human body model are discussed. The shortcomings of the method and possible directions for future work are outlined.

• Zhakatayev, Altay; Avazov, Nurilla; Rogovchenko, Yuriy & Pätzold, Matthias Uwe (2023). Human Motion Synthesis Using Trigonometric Splines. IEEE Access. ISSN 2169-3536. 11, p. 14293–14308. doi: 10.1109/ACCESS.2023.3244062. Full text in Research Archive Show summary

  In this work, we present a simple framework to synthesize human motion. Our main goal is to propose a methodology tailored for inexperienced users to initiate their research in human motion simulation and human motion trajectory optimization. The novelties of the work include the following. First, trigonometric splines are used instead of traditional B-splines to discretize the generalized coordinates and velocities. Second, useful identities for trigonometric splines are derived. Third, this work is intended to be an example, so that even non-skilled users, such as undergraduate students, can perform human motion analysis using a high-level programming language such as MATLAB. Four simulations of human motion are generated: walking, sitting and standing, side-step, and jump. The results of the walking simulation are validated by experiments. Simulation and experimental results are presented and discussed.

• Zhakatayev, Altay; Rogovchenko, Yuriy & Pätzold, Matthias Uwe (2022). Synthesis of Lower-Body Human Walking using Trigonometric Spline Method, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE conference proceedings. ISSN 979-8-3503-0979-9. p. 358–363. doi: 10.1109/Humanoids53995.2022.10000253. Show summary

  In this work, preliminary results of human motion synthesis are presented. Specifically, a single stride motion (consisting of two steps) of a human lower-body model is obtained. The optimal control problem was reformulated as a nonlinear programming problem using the differential inclusion method. The main goal of this study is to compare the performance of trigonometric and polynomial (B-spline) discretization methods. The obtained results indicate that the trigonometric spline method performs similarly to the B-spline method and results in a smooth motion.

• Avazov, Nurilla; Hicheri, Rym & Pätzold, Matthias Uwe (2022). Micro-Doppler Characteristics of mmWave Indoor Backscattering Channels for RF Sensing, 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-6654-6975-3. p. 357–362. doi: 10.1109/WiMob55322.2022.9941610. Show summary

  In indoor environments, backscattering components play a key role in radio-frequency (RF) sensing, especially with the blockage of line-of-sight components by the furniture. Thus, it is paramount to investigate the Doppler characteristics of backscattering millimeter wave (mmWave) channels in the presence of moving objects. In this regard, this paper presents a trajectory-driven non-stationary channel model for a mmWave single-input single-output communication system. This backscattering channel model takes into account the impact of a single moving object on the indoor radio wave propagation phenomena. First, the expression of the complex channel gain is presented. Then, the time-variant (TV) micro-Doppler signatures of the moving object are studied. The validity of the proposed channel model is confirmed by real-world measured RF data, which were collected in a laboratory room using a software defined radar system operating at 24 GHz. For fair comparison, we consider as a moving object a swinging pendulum for which the exact TV trajectory is known. In addition, inertial measurement unit sensors are attached to the pendulum to track its TV trajectory. A good agreement can be seen between the Doppler characteristics of the backscattering channel model and those computed from the measured RF data. This demonstrates that backscattering components can be employed to accurately extract the microDoppler signatures of a moving object in indoor spaces. The exhibited results can serve as a basis for the development of future robust backscattering-based RF sensing.

• Toma, Eduard Valentin; Toma, Anca Mihaela Ruxandra; Sterian, Corneliu Eugen & Pätzold, Matthias Uwe (2022). NOVEL 2X2 FULL-RATE SPACE-TIME BLOCK CODE WITH IMPROVED PERFORMANCE AT SMALL SEPARATION BETWEEN COLLOCATED ANTENNAS. Revue Roumaine des Sciences Techniques - Serie Electrotechnique et Energetique. ISSN 0035-4066. 67(2), p. 171–173. Show summary

  In this paper, we introduce a novel full rate 2X2 space-time block code and compare its FER and BER performance against that of the Golden code by way of computer simulations using a mathematical model that takes the distance between collocated antennas as a parameter. For d = 1 λ, where λ is the wavelength of the carrier frequency, the superiority of the new code is obvious. For d = 2 λ, the Golden code is better, but the new space-time block code still works rather well. While we do not provide here plots for d = λ/2, we know from our experiments that our new code shows only a gracious degradation as compared to the case d = 1 λ. This recommends the new code for applications where a small distance between collocated antennas is unavoidable. The main originality of the new code is using two signal constellations instead of a single one, as detailed in the body of our work. In so doing, we also greatly simplified the issue of determining the separators and the shapers, which are rather complex in the case of the Golden code.

• Muaaz, Muhammad; Waqar, Sahil & Pätzold, Matthias Uwe (2022). Radar-Based Passive Step Counter and Its Comparison with a Wrist-Worn Physical Activity Tracker. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 259–272. doi: 10.1007/978-3-031-10525-8_21. Show summary

  Inspired by novel applications of radio-frequency sensing in healthcare, smart homes, rehabilitation, and augmented reality, we present an FMCW radar-based passive step counter. If a person walks or performs other activities, the individual body segments, such as head, torso, legs, arms, and feet, move at different radial speeds. Owing to the Doppler effect, the individual body segments in motion cause distinct Doppler shifts that can be used to recognize and analyze the performed activities. We compute the time-variant Doppler spectrogram of a walking activity of a person and extract the high energy Doppler components that mainly describe the torso movements during walking. From the computed Doppler spectrogram, we then compute the mean Doppler shift. To detect and count steps, we apply the peak detection algorithm to the mean Doppler shift. Our approach is evaluated using a walking activity data set. We have used ground truths and a commercially available wristworn human activity tracker to validate the results of our approach. Our results show that our system is capable of passively counting 97.71%– 98.51% steps within a 12 m range. Therefore, our proposed system can be used as a passive step counter in indoor environments. Besides, it can also contribute to indoor localization and human tracking applications

• Waqar, Sahil; Muaaz, Muhammad & Pätzold, Matthias Uwe (2022). Human activity signatures captured under different directions using SISO and MIMO radar systems. Applied Sciences. ISSN 2076-3417. 12(4). doi: 10.3390/app12041825. Full text in Research Archive Show summary

  In this paper, we highlight and resolve the shortcomings of single-input single-output (SISO) millimeter wave (mm-Wave) radar systems for human activity recognition (HAR). A 2 × 2 distributed multiple-input multiple-output (MIMO) radar framework is presented to capture human activity signatures under realistic conditions in indoor environments. We propose to distribute the two pairs of collocated transmitter–receiver antennas in order to illuminate the indoor environment from different perspectives. For the proposed MIMO system, we measure the time-variant (TV) radial velocity distribution and TV mean radial velocity to observe the signatures of human activities. We deploy the Ancortek SDR-KIT 2400T2R4 mm-Wave radar in a SISO as well as a 2 × 2 distributed MIMO configuration. We corroborate the limitations of SISO configurations by recording real human activities in different directions. It is shown that, unlike the SISO radar configuration, the proposed MIMO configuration has the ability to obtain superior human activity signatures for all directions. To signify the importance of the proposed 2 × 2 MIMO radar system, we compared the performance of a SISO radar-based passive step counter with a distributed MIMO radar-based passive step counter. As the proposed 2 × 2 MIMO radar system is able to detect human activity in all directions, it fills a research gap of radio frequency (RF)-based HAR systems.

• Zhakatayev, Altay; Rogovchenko, Yuriy & Pätzold, Matthias Uwe (2021). Influence of generalized coordinates on system dynamics. In Kövecses, József; Stépán, Gábor & Zelei, Ambrus (Ed.), Proceedings of the 10th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS. Budapest University of Technology and Economics. ISSN 978-963-421-870-8. p. 235–244. doi: 10.3311/ECCOMASMBD2021-112. Show summary

  We investigate the effect of the choice of a set of generalized coordinates (GCs) on the simulation of the behavior of the dynamical system using the single-link spherical pendulum as an example. Specifically, we focus our attention on numerical errors and the simulation time necessary to simulate system dynamics. The Lagrangian method is applied to obtain the equations of motion. The generalized Euler angles are used as GCs. The GCs depend on the direction of the axes along which they are defined. Therefore, by parameterizing the directions of these two axes, different sets of GCs with the corresponding system of nonlinear differential equations are obtained. For a spherical pendulum, we demonstrate that the optimal sets of GCs leading to the minimum simulation time are orthogonal sets. However, contrary to our expectations, orthogonal sets do not result in the minimum simulation error. Additionally, the intrinsic generalized Euler angles lead to faster simulations than the extrinsic ones. Therefore, different choices of GCs are not equivalent from a numerical point of view and further research is needed to develop a strategy for selecting an optimal set of GCs.

• Hajri, Nazih; Pätzold, Matthias Uwe; Hicheri, Rym & Youssef, Neji (2021). Performance Analysis of M-DPSK Modulation over Fast-Hoyt Fading Channels under Non-Isotropic Scattering Conditions, 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE conference proceedings. ISSN 978-1-7281-7586-7. doi: 10.1109/PIMRC50174.2021.9569327.
• Hicheri, Rym; Avazov, Nurilla; Muaaz, Muhammad & Pätzold, Matthias Uwe (2021). The Transfer Function of Non-Stationary Indoor Channels and its Relationship to System Functions of LFMCW Radars, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE conference proceedings. ISSN 978-1-6654-2851-4. doi: 10.1109/SPAWC51858.2021.9593156.
• Waqar, Sahil & Pätzold, Matthias Uwe (2021). Interchannel Interference and Mitigation in Distributed MIMO RF Sensing. Sensors. ISSN 1424-8220. 21(22). doi: 10.3390/s21227496. Full text in Research Archive
• Avazov, Nurilla; Hicheri, Rym & Pätzold, Matthias Uwe (2021). A Trajectory-Driven SISO mm-Wave Channel Model for a Human Activity Recognition, 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-6654-2854-5. doi: 10.1109/WiMob52687.2021.9606320.
• Hicheri, Rym; Muaaz, Muhammad; Avazov, Nurilla & Pätzold, Matthias Uwe (2021). On the Estimation of the Radial Distance of a Moving Person in Indoor Environments from the Demodulated Response of LFMCW Radars, 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-6654-2854-5. doi: 10.1109/WiMob52687.2021.9606432.
• Avazov, Nurilla; Hicheri, Rym; Muaaz, Muhammad; Sanfilippo, Filippo & Pätzold, Matthias Uwe (2021). A Trajectory-Driven 3D Non-Stationary mm-Wave MIMO Channel Model for a Single Moving Point Scatterer. IEEE Access. ISSN 2169-3536. 9, p. 115990–116001. doi: 10.1109/ACCESS.2021.3105296. Full text in Research Archive
• Chelli, Ali; Muaaz, Muhammad; Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2021). Human activity recognition using Wi-Fi and machine learning, Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence. CRC Press. ISSN 9781003181545. p. 75–80.
• Hicheri, Rym; Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2021). A new non-stationary 3D channel model with time-variant path gains for indoor human activity recognition, Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence. CRC Press. ISSN 9781003181545. p. 68–74.
• Hicheri, Rym & Pätzold, Matthias Uwe (2021). An RF-Based Positioning Method for Tracing a Cluster of Moving Scatterers in Non-Stationary Indoor Environments. IEEE Wireless Communications Letters. ISSN 2162-2337. 10(9). doi: 10.1109/LWC.2021.3084242. Full text in Research Archive
• Hicheri, Rym; Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2021). A non-stationary relay-based 3D MIMO channel model with time-variant path gains for human activity recognition in indoor environments. Annales des télécommunications. ISSN 0003-4347. 76, p. 827–837. doi: 10.1007/s12243-021-00844-0. Full text in Research Archive
• Abdelgawwad, Ahmed; Catala, Andreu & Pätzold, Matthias Uwe (2021). A Trajectory-Driven 3D Channel Model for Human Activity Recognition. IEEE Access. ISSN 2169-3536. 9, p. 103393–103406. doi: 10.1109/ACCESS.2021.3098951. Full text in Research Archive
• Muaaz, Muhammad; Chelli, Ali; Gerdes, Martin & Pätzold, Matthias Uwe (2021). Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems. Annales des télécommunications. ISSN 0003-4347. doi: 10.1007/s12243-021-00865-9. Full text in Research Archive
• Avazov, Nurilla; Hicheri, Rym & Pätzold, Matthias Uwe (2021). A Trajectory-Driven SIMO mm-Wave Channel Model for a Moving Point Scatterer, 2021 15th European Conference on Antennas and Propagation (EuCAP). IEEE conference proceedings. ISSN 978-88-31299-02-2. doi: 10.23919/EuCAP51087.2021.9411210. Full text in Research Archive
• Muaaz, Muhammad; Chelli, Ali & Pätzold, Matthias Uwe (2021). Wi-Fi-based human activity recognition using convolutional neural network. In Slama, Sami Ben; Choubani, Fethi; Benavente-Peces, César & Abdelkarim, Afef (Ed.), Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence. CRC Press. ISSN 9781003181545. doi: 10.1201/9781003181545-11.
• Pätzold, Matthias Uwe (2020). The role of 5G in limiting the impact of the COVID-19 pandemic . IEEE Vehicular Technology Magazine. ISSN 1556-6072. doi: 10.1109/MVT.2020.3023361.
• Pätzold, Matthias Uwe (2020). Tackling global environmental challenges through ICT. IEEE Vehicular Technology Magazine. ISSN 1556-6072. doi: 10.1109/MVT.2020.3002635.
• Pätzold, Matthias Uwe (2020). Toward Realizing the Full Potential of a 5G-Empowered World. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 15(1), p. 5–11. doi: 10.1109/MVT.2019.2956891.
• Hicheri, Rym; Pätzold, Matthias Uwe & Youssef, Neji (2020). Estimation of the Time-Variant Velocity of a Single Walking Person in Two-Dimensional Non-Stationary Indoor Environments Using Radio-Frequency Techniques. IEEE Access. ISSN 2169-3536. 8, p. 169759–169774. doi: 10.1109/ACCESS.2020.3023815. Full text in Research Archive
• Muaaz, Muhammad; Chelli, Ali; Abdelgawwad, Ahmed; Catala Mallofré, Andreu & Pätzold, Matthias Uwe (2020). WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities. IEEE Access. ISSN 2169-3536. 8. doi: 10.1109/ACCESS.2020.3022287. Full text in Research Archive
• Chelli, Ali; Muaaz, Muhammad & Pätzold, Matthias Uwe (2020). ActRec: A Wi-Fi-based human activity recognition system, 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7440-2. doi: 10.1109/ICCWorkshops49005.2020.9145361.
• Pätzold, Matthias Uwe (2020). 5G Unlocks Its Power for Global Mobile Connectivity. IEEE Vehicular Technology Magazine. ISSN 1556-6072. doi: 10.1109/MVT.2020.2979100.
• Peng, Bile; Guan, Ke; Küter, Alexander; Rey, Sebastian; Pätzold, Matthias Uwe & Kürner, Thomas (2020). Channel Modeling and System Concepts for Future Terahertz Communications: Getting Ready for Advances Beyond 5G. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 15(2), p. 136–143. doi: 10.1109/MVT.2020.2977014.
• Insausti, Xabier; Hogstad, Bjørn Olav & Pätzold, Matthias Uwe (2020). Modelling and Simulation of Ego-Noise of Unmanned Aerial Vehicles. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2020-Spring48590.2020.9128572.
• Muaaz, Muhammad; Chelli, Ali & Pätzold, Matthias Uwe (2020). WiHAR: From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2020-Spring48590.2020.9128418. Full text in Research Archive
• Abdelgawwad, Ahmed; Catala, Andreu & Pätzold, Matthias Uwe (2020). Doppler Power Characteristics Obtained from Calibrated Channel State Information for Human Activity Recognition. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2020-Spring48590.2020.9129187. Full text in Research Archive
• Abdelgawwad, Ahmed; Borhani, Alireza & Pätzold, Matthias Uwe (2020). Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments . Sensors. ISSN 1424-8220. 20(4). doi: 10.3390/s20041049. Full text in Research Archive
• Gutierrez, Carlos Adrian; Ornelas-Lizcano, Juan C. & Pätzold, Matthias Uwe (2019). Geometrical Modeling of Non-Stationary Polarimetric Vehicular Radio Channels, 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE conference proceedings. ISSN 978-1-7281-3616-5. doi: 10.1109/CAVS.2019.8887811. Full text in Research Archive
• Hellaoui, Hamed; Chelli, Ali; Bagaa, Miloud; Taleb, Tarik & Pätzold, Matthias Uwe (2019). Towards Efficient Control of Mobile Network-Enabled UAVs. IEEE Wireless Communications and Networking Conference. ISSN 1525-3511. 2019-April. doi: 10.1109/WCNC.2019.8885665.
• Hicheri, Rym & Pätzold, Matthias Uwe (2019). A New Iterative Procedure for the Localization of a Moving Object/Person in Indoor Areas from Received RF Signals, 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-7281-3316-4. doi: 10.1109/WiMOB.2019.8923428. Full text in Research Archive
• Hicheri, Rym & Pätzold, Matthias Uwe (2019). A New Iterative Estimation Procedure for the Localization of Passive Stationary Objects from Received RF Signals in Indoor Environments, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). IEEE conference proceedings. ISSN 978-1-7281-1220-6. doi: 10.1109/VTCFall.2019.8891440. Full text in Research Archive
• Chelli, Ali & Pätzold, Matthias Uwe (2019). Fall Detection Based on the Instantaneous Doppler Frequency: A Machine Learning Approach, 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE conference proceedings. ISSN 978-1-5386-7646-2. doi: 10.1109/WCNC.2019.8885692. Full text in Research Archive
• Chelli, Ali & Pätzold, Matthias Uwe (2019). A Machine Learning Approach for Fall Detection Based on the Instantaneous Doppler Frequency. IEEE Access. ISSN 2169-3536. 7, p. 166173–166189. doi: 10.1109/ACCESS.2019.2947739. Full text in Research Archive
• Gutierrez, Carlos Adrian; Pätzold, Matthias Uwe; Ortega, Nicolas N.; Azurdia-Meza, Cesar A. & Barboza, Fermin Marcelo Ruben Marciel (2019). Doppler Shift Characterization of Wideband Mobile Radio Channels. IEEE Transactions on Vehicular Technology. ISSN 0018-9545. 68(12), p. 12375–12380. doi: 10.1109/TVT.2019.2945936. Full text in Research Archive
• Pätzold, Matthias Uwe (2019). 5G Sets Sail Around the Globe. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 14(2). doi: 10.1109/MVT.2019.2902773.
• Pätzold, Matthias Uwe (2019). Myth and Reality of 5G Communications. IEEE Vehicular Technology Magazine. ISSN 1556-6072. doi: 10.1109/MVT.2019.2919852.
• Borhani, Alireza; Pätzold, Matthias Uwe & Yang, Kun (2019). Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant. Sensors. ISSN 1424-8220. 19(16). doi: 10.3390/s19163557. Full text in Research Archive Show summary

  While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequency characteristic of the channel has been thoroughly investigated by spectrogram analysis. Studying the instantaneous Doppler characteristics shows that the Doppler spread ignores small variations of the channel (especially under OLOS conditions), but highlights coarse ones caused by falls. The channel properties studied in this paper can be considered to be new useful metrics for the design of reliable fall detection algorithms. We share all measured data files with the community through Code Ocean. The data can be used for validating a new class of channel models aiming at the design of smart activity recognition systems via a software-based approach.

• Muaaz, Muhammad; Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2019). The Influence of Human Walking Activities on the Doppler Characteristics of Non-stationary Indoor Channel Models, Advances in Computational Intelligence, 15th Int. Work-Conference on Artificial Neural Networks, IWANN 2019, Part I. Springer. ISSN 978-3-030-20520-1. p. 297–309. doi: 10.1007/978-3-030-20521-8_25. Full text in Research Archive Show summary

  This paper analyzes the time-variant (TV) Doppler power spectral density of a 3D non-stationary fixed-to-fixed indoor channel simulator after feeding it with realistic trajectories of a walking person. The trajectories of the walking person are obtained by simulating a full body musculoskeletal model in OpenSim. We provide expressions of the TV Doppler frequencies caused by these trajectories. Then, we present the complex channel gain consisting of fixed scatterers and a cluster of moving scatterers. After that, we use the concept of the spectrogram to analyze the TV Doppler power spectral density of the complex channel gain. Finally, we present expressions of the TV mean Doppler shift and Doppler spread. The work of this paper is important for human activity recognition systems using radio-frequency (non-wearable) sensors as the demand for such systems has increased nowadays.

• Borhani, Alireza & Pätzold, Matthias Uwe (2019). RF-Based Human Activity Recognition: A Non-stationary Channel Model Incorporating the Impact of Phase Distortions, Advances in Computational Intelligence, 15th Int. Work-Conference on Artificial Neural Networks, IWANN 2019, Part I. Springer. ISSN 978-3-030-20520-1. p. 335–346. doi: 10.1007/978-3-030-20521-8_28. Full text in Research Archive Show summary

  This paper proposes a non-stationary channel model that captures the impact of the time-variant (TV) phase distortion caused by hardware imperfections. The model allows for studying the spectrogram of in-home radio channels influenced by walking activities of the home user under realistic non-stationary propagation conditions. The resolution of the spectrogram is investigated for the von-Mises distribution of the phase distortion. It is shown that high-entropy distributions considerably mask fingerprints of the user activity on the spectrogram of the channel. For an orthogonal frequency-division multiplexing (OFDM) system, a computationally simple method for mitigating the undesired phase rotation is proposed. Both theoretical and simulation results confirm that the proposed method significantly reduces the impact of the phase distortion, allowing us to retrieve the desired spectrogram imprinted by the activity of the home user. The results of this paper are useful for the development of software-based radio frequency (RF)-based activity recognition systems.

• Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2019). A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. 2019-April, p. 1–7. doi: 10.1109/VTCSpring.2019.8746345. Full text in Research Archive
• Hicheri, Rym & Pätzold, Matthias Uwe (2019). Estimation of the Velocity of Multiple Moving Persons in Non-Stationary Indoor Environments from Received RF Signals. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. 2019-April. doi: 10.1109/VTCSpring.2019.8746693. Full text in Research Archive
• Pätzold, Matthias Uwe (2019). 5G Is Coming Around the Corner [Mobile Radio]. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 14(1). doi: 10.1109/MVT.2018.2884042.
• Chelli, Ali & Pätzold, Matthias Uwe (2019). A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition. IEEE Access. ISSN 2169-3536. 7, p. 38670–38687. doi: 10.1109/ACCESS.2019.2906693. Full text in Research Archive
• Borhani, Alireza & Pätzold, Matthias Uwe (2018). The Impact of Human Walking on the Time-Frequency Distribution of In-Home Radio Channels, 2018 Asia-Pacific Microwave Conference (APMC) Proceedings. IEEE conference proceedings. ISSN 978-4-902339-45-1. doi: 10.23919/APMC.2018.8617253. Full text in Research Archive
• Hicheri, Rym; Pätzold, Matthias Uwe & Youssef, Neji (2018). Estimation of the Velocity of a Walking Person in Non-Stationary Indoor Environments from the Received RF Signal, LATINCOM 2018. IEEE Sarnoff Symposium. ISSN 978-1-5386-6754-5. doi: %2010.1109/LATINCOM.2018.8613224. Full text in Research Archive
• Hicheri, Rym; Pätzold, Matthias Uwe & Youssef, Neji (2018). Estimation of the Velocity of a Walking Person in Indoor Environments from mmWave Signals, GLOBECOM 2018. IEEE Sarnoff Symposium. ISSN 978-1-5386-4920-6. doi: 10.1109/GLOCOMW.2018.8644308. Full text in Research Archive Show summary

  The present work is motivated by the growing interest in using millimeter-wave (mmWave) bands in future wireless indoor communications. For a variety of wireless indoor applications, such as remote medical care, healthcare services, and human-machine interaction, it is of crucial importance to estimate the velocity of walking persons in indoor environments with high precision. In this paper, we present a novel procedure for estimating the velocity of a walking person in indoor environments by using mmWave signals. The indoor environment is considered to be equipped with a distributed 2x2 multiple-input multiple-output (MIMO) system operating in the 60 GHz band. The proposed approach requires first to estimate the time-variant Doppler frequencies by fitting the spectrogram of the complex channel gain of a non-stationary indoor channel model to the spectrogram obtained from the received mmWave signals. The velocity of the moving person is then deduced from estimated time-varying Doppler frequencies. Numerical results, comparing the estimated Doppler frequencies and velocity with corresponding exact values, known from generated mmWave signals, are provided to demonstrate the validity of the proposed velocity estimation algorithm.

• Pätzold, Matthias Uwe (2018). It's time to go big with 5G. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 13(4), p. 4–10. doi: 10.1109/MVT.2018.2869728.
• Pätzold, Matthias Uwe (2018). The Final Sprint of Fifth-Generation Toward Commercialization. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 13(3), p. 4–21. doi: 10.1109/MVT.2018.2849038.
• Chelli, Ali & Pätzold, Matthias Uwe (2018). Recognition of Falls and Daily Living Activities Using Machine Learning. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops. ISSN 2166-9570. doi: 10.1109/PIMRC.2018.8580874. Full text in Research Archive Show summary

  A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm.

• Borhani, Alireza & Pätzold, Matthias Uwe (2018). A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 17(11), p. 7718–7730. doi: 10.1109/TWC.2018.2869782. Full text in Research Archive Show summary

  The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a nonstationary channel model that is important for the development of such systems. A three-dimensional stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modelled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behaviour of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.

• Pätzold, Matthias Uwe & Gutierrez, Carlos Adrian (2018). Modelling of Non-WSSUS Channels with Time-Variant Doppler and Delay Characteristics, 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE). IEEE conference proceedings. ISSN 978-1-5386-3677-0. p. 145–150. doi: 10.1109/CCE.2018.8465761. Full text in Research Archive
• Pätzold, Matthias Uwe & Gutierrez, Carlos Adrian (2018). Definition and Analysis of Quasi-Stationary Intervals of Mobile Radio Channels, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE conference proceedings. ISSN 978-1-5386-6355-4. doi: 10.1109/VTCSpring.2018.8417467. Full text in Research Archive
• Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2018). A Framework for Activity Monitoring and Fall Detection Based on the Characteristics of Indoor Channels, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE conference proceedings. ISSN 978-1-5386-6355-4. doi: 10.1109/VTCSpring.2018.8417468. Full text in Research Archive Show summary

  This paper concerns the Doppler power spectrum of three-dimensional non-stationary indoor fixed-to- fixed channels with moving people. In this paper, we model each moving person as a moving scatterer with time-variant (TV) speed, TV vertical angles of motion, and TV horizontal angles of motion of the moving scatterers. Furthermore, we derive the TV angular parameters of each moving scatterer such as the elevation angle of departure, the azimuth angle of departure, the elevation angle of arrival, and the azimuth angle of arrival. In addition, the TV unit vectors of the departure of the transmitted wave planes and unit vectors of the arrival of the received wave planes are derived. Furthermore, to present the Doppler power spectrum characteristics of such channels, we provide the closed-form solution of the spectrogram of the complex channel gain. The correctness of the analysis is approved by simulations. The contribution of this paper is an initiative for the development of device-free indoor activity monitoring and fall detection systems.

• Pätzold, Matthias Uwe (2018). Countdown for the Full-Scale Development of 5G New Radio [Mobile Radio]. IEEE Vehicular Technology Magazine. ISSN 1556-6072. 13(2), p. 7–13. doi: 10.1109/MVT.2018.2811912.
• Hajri, Nazih; Youssef, Neji; Kawabata, Tsutomu; Pätzold, Matthias Uwe & Dahech, Wiem (2018). Statistical Properties of Double Hoyt Fading With Applications to the Performance Analysis of Wireless Communication Systems. IEEE Access. ISSN 2169-3536. 6, p. 19597–19609. doi: 10.1109/ACCESS.2018.2820746.
• Pätzold, Matthias Uwe & Gutierrez, Carlos Adrian (2018). Modelling and Analysis of Nonstationary Vehicle-to-Infrastructure Channels with Time-Variant Angles of Arrival. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2018. doi: 10.1155/2018/6396173. Full text in Research Archive
• Ma, Yuanyuan; Hogstad, Bjørn Olav; Pätzold, Matthias Uwe & Crespo, Pedro M. (2018). Statistical modeling, simulation, and experimental verification of wideband indoor mobile radio channels. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2018:8271765, p. 1–14. doi: 10.1155/2018/8271765. Full text in Research Archive

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• Pätzold, Matthias Uwe (2023). New Paradigms for the Design of Human Activity Recognition Systems Using Radar Sensors.
• Pätzold, Matthias Uwe (2023). New Paradigms for the Design of Human Activity Recognition Systems Using Radar Sensors.
• Pätzold, Matthias Uwe (2023). Paradigm Shifts in Human Activity Recognition Using Principles of Radar Sensing.
• Pätzold, Matthias Uwe (2022). Cooperative Human Activity Recognition and Localization for Healthcare and Wellbeing .
• Muaaz, Muhammad; Waqar, Sahil & Pätzold, Matthias Uwe (2021). Radar-based passive step counter and its comparison with a wrist-worn physical activity tracker.
• Chelli, Ali; Muaaz, Muhammad & Pätzold, Matthias Uwe (2020). ActRec: A Wi-Fi-based human activity recognition system.
• Muaaz, Muhammad; Chelli, Ali & Pätzold, Matthias Uwe (2020). WiHAR: From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition.
• Hicheri, Rym; Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2020). A new non-stationary cluster-based 3D channel model for indoor human activity recognition.
• Abdelgawwad, Ahmed; Catala, Andreu & Pätzold, Matthias Uwe (2020). Doppler Power Characteristics Obtained from Calibrated Channel State Information for Human Activity Recognition .
• Pätzold, Matthias Uwe (2020). Modelling, Analysis, and Simulation of Non-Stationary Mobile Radio Channels with Applications in Wireless Sensing for Human Activity Recognition.
• Pätzold, Matthias Uwe (2020). Ny teknologi skal hjelpe eldre å bu heime lenger. [Internet]. Universitetet i Agder.
• Pätzold, Matthias Uwe (2020). Utviklar teknologi for ein trygg heim. [Internet]. Universitetet i Agder. Show summary

  Ny teknologi utvikla av IKT-miljøet på Universitetet i Agder skal få eldre til å føle seg tryggare i heimen.

• Pätzold, Matthias Uwe (2019). Probability Theory and Stochastic Processes.
• Muaaz, Muhammad & Pätzold, Matthias Uwe (2019). The influence of human walking activities¨on the Doppler characteristics of non-stationary indoor channel models.
• Borhani, Alireza & Pätzold, Matthias Uwe (2019). RF-based human activity recognition: A non-stationary channel model incorporating the impact of phase distortions.
• Hicheri, Rym & Pätzold, Matthias Uwe (2019). Estimation of the velocity of multiple moving persons in non-stationary indoor environments from the received RF signal.
• Hicheri, Rym & Pätzold, Matthias Uwe (2019). A new iterative estimation procedure for the localization of passive stationary objects from received RF signals in indoor environments.
• Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2019). A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition.
• Pätzold, Matthias Uwe (2019). Modelling of Non-Stationary Mobile Radio Channels with Applications in Activity Recognition.
• Hicheri, Rym; Pätzold, Matthias Uwe & Youssef, Neji (2018). Estimation of the velocity of a walking person in non-stationary indoor environments from the received RF signal.
• Borhani, Alireza & Pätzold, Matthias Uwe (2018). The impact of human walking on the time-frequency distribution of in-home radio channels.
• Hicheri, Rym; Pätzold, Matthias Uwe & Youssef, Neji (2018). Estimation of the Velocity of a Walking Person in Indoor Environments from mmWave Signals.
• Pätzold, Matthias Uwe (2018). Probability Theory and Stochastic Processes.
• Chelli, Ali & Pätzold, Matthias Uwe (2018). Recognition of Falls and Daily Living Activities Using Machine Learning.
• Pätzold, Matthias Uwe & Gutierrez, Carlos Adrian (2018). Modelling of Non-WSSUS Channels with Time-Variant Doppler and Delay Characteristics.
• Pätzold, Matthias Uwe & Gutierrez, Carlos Adrian (2018). Definition and Analysis of Quasi-Stationary Intervals of Mobile Radio Channels.
• Pätzold, Matthias Uwe (2018). Status of the WiCare Project.
• Pätzold, Matthias Uwe (2018). Modelling and Analysis of Non-Stationary Radio Channels with Applications in Human Activity.
• Pätzold, Matthias Uwe (2018). Modelling and Analysis of Non-Stationary Radio Channels with Applications in Human Activity Recognition.
• Abdelgawwad, Ahmed & Pätzold, Matthias Uwe (2021). Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems. Media 07. ISSN 978-82-8427-039-5. Full text in Research Archive

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