With an aging population, numerous assistive and monitoring technologies are
under development to enable older adults to age in place. To facilitate aging
in place predicting risk factors such as falls, and hospitalization and
providing early interventions are important. Much of the work on ambient
monitoring for risk prediction has centered on gait speed analysis, utilizing
privacy-preserving sensors like radar. Despite compelling evidence that
monitoring step length, in addition to gait speed, is crucial for predicting
risk, radar-based methods have not explored step length measurement in the
home. Furthermore, laboratory experiments on step length measurement using
radars are limited to proof of concept studies with few healthy subjects. To
address this gap, a radar-based step length measurement system for the home is
proposed based on detection and tracking using radar point cloud, followed by
Doppler speed profiling of the torso to obtain step lengths in the home. The
proposed method was evaluated in a clinical environment, involving 35 frail
older adults, to establish its validity. Additionally, the method was assessed
in people's homes, with 21 frail older adults who had participated in the
clinical assessment. The proposed radar-based step length measurement method
was compared to the gold standard Zeno Walkway Gait Analysis System, revealing
a 4.5cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent
reliability (ICC(2,k)=0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings.
The method also proved accurate in uncontrolled home settings, as indicated by
a strong agreement (ICC(3,k)=0.81 (95% CI 0.53 to 0.92)) between home
measurements and in-clinic assessments.

通过雷达点云检测和跟踪以及多普勒速度剖面技术，在医疗环境和家庭环境中验证了一种基于雷达的步长测量方法，该方法在临床设置中的误差仅为 4.5 厘米 / 8.3%，在非受控家庭环境中显示出良好的可靠性和准确性。

野外使用 FMCW 雷达进行步长测量

Step length measurement in the wild using FMCW radar

In dynamic environments, Q-learning is an adaptative rule that provides an
estimate (a Q-value) of the continuation value associated with each
alternative. A naive policy consists in always choosing the alternative with
highest Q-value. We consider a family of Q-based policy rules that may
systematically favor some alternatives over others, for example rules that
incorporate a leniency bias that favors cooperation. In the spirit of Compte
and Postlewaite [2018], we look for equilibrium biases (or Qb-equilibria)
within this family of Q-based rules. We examine classic games under various
monitoring technologies.

本文研究了一类基于 Q 学习的策略规则，在不同的监控技术下，寻找平衡偏差以及如何将偏好修正到 Q 值中，以实现更有效的合作和均衡策略。