Erik M. Olson

Bio: Erik M. Olson is an academic researcher from Google. The author has contributed to research in topics: Radar & Gesture recognition. The author has an hindex of 4, co-authored 9 publications receiving 578 citations. Previous affiliations of Erik M. Olson include Stanford University.

Soli: ubiquitous gesture sensing with millimeter wave radar

Abstract: This paper presents Soli, a new, robust, high-resolution, low-power, miniature gesture sensing technology for human-computer interaction based on millimeter-wave radar. We describe a new approach to developing a radar-based sensor optimized for human-computer interaction, building the sensor architecture from the ground up with the inclusion of radar design principles, high temporal resolution gesture tracking, a hardware abstraction layer (HAL), a solid-state radar chip and system architecture, interaction models and gesture vocabularies, and gesture recognition. We demonstrate that Soli can be used for robust gesture recognition and can track gestures with sub-millimeter accuracy, running at over 10,000 frames per second on embedded hardware.

RF-Based Micro-Motion Tracking for Gesture Tracking and Recognition

Abstract: This document describes techniques for radio frequency (RF) based micro-motion tracking. These techniques enable even millimeter-scale hand motions to be tracked. To do so, radar signals are used from radar systems that, with conventional techniques, would only permit resolutions of a centimeter or more.

Radar modulation for radar sensing using a wireless communication chipset

Abstract: Techniques and apparatuses are described that enable radar modulations for radar sensing using a wireless communication chipset. A controller initializes or controls modulations performed by the wireless communication chipset. In this way, the controller can enable the wireless communication chipset to perform modulations for wireless communication or radar sensing. In some cases, the controller can further select a wireless communication channel for setting a frequency and a bandwidth of a radar signal, thereby avoiding interference between multiple radar signals or between the radar signal and a communication signal. In other cases, the controller can cause the wireless communication chipset to modulate a signal containing communication data using a radar modulation. This enables another device that receives the signal to perform wireless communication or radar sensing. By utilizing these techniques, the wireless communication chipset can be used for wireless communication or radar sensing.

Smartphone-based radar system detecting user gestures using coherent multi-look radar processing

Abstract: Techniques and apparatuses are described that implement a smartphone-based radar system capable of detecting user gestures using coherent multi-look radar processing. Different approaches use a multi-look interferometer or a multi-look beamformer to coherently average multiple looks of a distributed target across two or more receive channels according to a window that spans one or more dimensions in time, range, or Doppler frequency. By coherently averaging the multiple looks, a radar system generates radar data with higher gain and less noise. This enables the radar system to achieve higher accuracies and be implemented within a variety of different devices. With these accuracies, the radar system can support a variety of different applications, including gesture recognition or presence detection.

제스처 추적 및 인식을 위한 rf―기반 마이크로―모션 추적

Abstract: 본 문헌은 RF(radio frequency) 기반 마이크로-모션 추적을 위한 기술들을 설명한다. 이러한 기술들은 심지어 밀리미터-스케일의 손 모션들이 추적되는 것을 가능하게 한다. 그렇게 하기 위해, 종래의 기술들을 사용하여 센티미터 또는 그 초과의 분해능들만을 허용할 레이더 시스템들로부터의 레이더 신호들이 사용된다.

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Abstract: Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum

Abstract: This paper proposes a novel machine learning architecture, specifically designed for radio-frequency based gesture recognition. We focus on high-frequency (60]GHz), short-range radar based sensing, in particular Google's Soli sensor. The signal has unique properties such as resolving motion at a very fine level and allowing for segmentation in range and velocity spaces rather than image space. This enables recognition of new types of inputs but poses significant difficulties for the design of input recognition algorithms. The proposed algorithm is capable of detecting a rich set of dynamic gestures and can resolve small motions of fingers in fine detail. Our technique is based on an end-to-end trained combination of deep convolutional and recurrent neural networks. The algorithm achieves high recognition rates (avg 87%) on a challenging set of 11 dynamic gestures and generalizes well across 10 users. The proposed model runs on commodity hardware at 140 Hz (CPU only).

Towards Environment Independent Device Free Human Activity Recognition

Abstract: Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

Device-Free Human Activity Recognition Using Commercial WiFi Devices

Abstract: Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes.

Toward Millimeter-Wave Joint Radar Communications: A signal processing perspective

Abstract: Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design.