Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine.

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.

A Size, Weight, Power, and Cost-Efficient 32-Channel Time to Digital Converter Using a Novel Wave Union Method.

We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a low-cost Field-Programmable Gate Array (FPGA), Artix-7, from Xilinx. Compared to prior works, our high-precision multi-channel TDC has the lowest SWaP-C requirements. We demonstrate an average time precision of less than 3 ps and a Root Mean Square resolution of about 1.81 ps. We propose a novel Wave Union type A architecture where only the first multiplexer is used to generate the wave union pulse train at the arrival of the start signal to minimize the required computational processing. In addition, an auto-calibration algorithm is proposed to help improve the TDC performance by improving the TDC Differential Non-Linearity and Integral Non-Linearity.

A Study on the Effect of Temperature Variations on FPGA-Based Multi-Channel Time-to-Digital Converters.

We describe a study on the effect of temperature variations on multi-channel time-to-digital converters (TDCs). The objective is to study the impact of ambient thermal variations on the performance of field-programmable gate array (FPGA)-based tapped delay line (TDL) TDC systems while simultaneously meeting the requirements of high-precision time measurement, low-cost implementation, small size, and low power consumption. For our study, we chose two devices, Artix-7 and ProASIC3L, manufactured by Xilinx and Microsemi, respectively. The radiation-tolerant ProASIC3L device offers better stability in terms of thermal sensitivity and power consumption compared to the Artix-7. To assess the performance of the TDCs under varying thermal conditions, a laboratory thermal chamber was utilized to maintain ambient temperatures ranging from -75 to 80 °C. This analysis ensured a comprehensive evaluation of the TDCs' performance across a wide operational range. By utilizing the Artix-7 and ProASIC3L devices, we achieved root mean square (RMS) resolution of 24.7 and 554.59 picoseconds, respectively. Total on-chip power of 0.968 W was achieved using Artix-7, while 1.997 mW of power consumption was achieved using the ProASIC3L device. We worked to determine the temperature sensitivity for both FPGA devices, which could help in the design and optimization of FPGA-based TDCs for many applications.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning.

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.