Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Statistical signal analysis and estimation algorithms for mud pulse telemetry systems

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Scattered Pilot-Based Channel Estimation for Channel Adaptive FBMC-OQAM Systems

Shaping the pulse of FilterBank MultiCarrier with Offset Quadrature Amplitude Modulation subcarrier modulation (FBMC-OQAM) systems offers a new degree of freedom for the design of mobile communication systems. In previous studies, we evaluated the gains arising from the application of Prototype Filter Functions (PFFs) and subcarrier spacing matched to the delay and Doppler spreads of doubly dispersive channels. In this paper, we investigate the impact of having imperfect channel knowledge at the receiver on the performance of Channel Adaptive Modulation (CAM) in terms of channel estimation errors and Bit Error Rate (BER). To this end, the channel estimation error for two different interference mitigation schemes proposed in the literature is derived analytically and its influence on the BER performance is analyzed for practical channel scenarios. The results show that FBMC-OQAM systems utilizing CAM and scattered pilot-based channel estimation provide a significant performance gain compared with the current one system design for a variety of channel scenarios ("one-fits-all") approach. Additionally, we verified that the often used assumption of a flat channel in the direct neighborhood of a pilot symbol is not valid for practical scenarios. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

On the efficiency of PAPR reduction schemes deployed for DRM systems

Digital Radio Mondiale (DRM) is the universally, openly standardized digital broadcasting system for all frequencies including LW, MW, and SW as well as VHF bands. Alongside providing high audio quality to listeners, DRM satisfies technological requirements posed by broadcasters, manufacturers and regulatory authorities and thus bears a great potential for the future of global radio. One of the key issues here concerns green broadcasting. Facing the need for high-power transmitters to cover wide areas, there is room for improvement concerning the power efficiency of DRM-transmitters. A major drawback of DRM is its high peak-to-average power ratio (PAPR) due to the applied transmission technology based on Orthogonal Frequency Division Multiplexing (OFDM), which results in non-linearities in the emitted signal, low power efficiency, and high costs of transmitters. To overcome this, numerous schemes have been investigated for reducing PAPR in OFDM systems. In this paper, we review and analyze various technologies to reduce PAPR providing that the technical feasibility and DRM-specific system architecture and edge conditions regarding the system performance in terms of modulation error rate, compliance with frequency mask, and synchronization efficiency are ensured. All evaluations are carried out with I/Q signals which are monitored in real operation to present the actual performance of proposed PAPR techniques. Subsequently, the capability of the best approach is evaluated via measurements on a DRM test platform, where achieved transmit power gain of 10 dB is shown. According to our evaluation results, PAPR reduction schemes based on active constellation extension followed by a filter prove to be promising towards practical realization of power-efficient transmitters. © 2016, The Author(s)

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system