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This paper used micro-Doppler radar (MDR) measurements to investigate the significance of associations between cognitive functions and gait features of elderly persons. The aim of this paper was to develop a system that would enable the risks of developing dementia and related diseases to be monitored remotely on a daily basis. Study participants were adults aged 75 years and older. Gait velocity parameters corresponding to the walking speed and leg and foot velocities were remotely extracted via a simple 24-GHz MDR system in real time. The relationships between the extracted gait velocity parameters and the global cognition and cognitive functions in various cognitive domains (processing speed, memory, executive function, and language domains) that were assessed by conventional paper-and question-based tests were statistically analyzed. Our results revealed that, apart from the walking speed, which was mainly considered in a previous study, other parameters reflecting the leg and foot velocities are effective for the detection and classification of elderly participants with lower cognitive functions in the various cognitive domains. In particular, the statistical significance of the association of the leg velocity in the swing phase with the results of all the cognitive function tests is larger than that of the walking speed. Another important finding is that different gait velocity parameters are associated with each cognitive domain and this means that the MDR-based gait measurement can be used to determine which cognitive domain has deteriorated.

This paper examines the performance of two position-velocity-measured (PVM) α-β-γ tracking filters. The first estimates the target acceleration using the measured velocity, and the second, which is proposed for the first time in this paper, estimates acceleration using the measured position. To quantify the performance of these PVM α-β-γ filters, we analytically derive steady-state errors that assume that the target is moving with constant acceleration or jerk. With these performance indices, the optimal gains of the PVM α-β-γ filters are determined using a minimum-variance filter criterion. The performance of each filter under these optimal gains is then analyzed and compared. Numerical analyses clarify the performance of the PVM α-β-γ filters and verify that their accuracy is better than that of the general position-only-measured α-β-γ filter, even when the variance in velocity measurement noise is comparatively large. We identify the conditions under which the proposed PVM α-β-γ filter outperforms the general α-β-γ filter for different ratios of noise variance in the velocity and position measurements. Finally, numerical simulations verify the effectiveness of the PVM α-β-γ filters for a realistic maneuvering target.

We present an automatic parameter setting method to achieve an accurate second-order Kalman filter tracker based on a steady-state performance index. First, we propose an efficient steady-state performance index that corresponds to the root-mean-square (rms) prediction error in tracking. We then derive an analytical relationship between the proposed performance index and the generalized error covariance matrix of the process noise, for which the automatic determination using the derived relationship is presented. The model calculated by the proposed method achieves better accuracy than the conventional empirical model of process noise. Numerical analysis and simulations demonstrate the effectiveness of the proposed method for targets with accelerating motion. The rms prediction error of the tracker designed by the proposed method is 63.8% of that with the conventional empirically selected model for a target accelerating at 10 m/s 2 .

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This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

Featured Application: Design and evaluation of monitoring systems in intelligent vehicles, robots, and so on.Abstract: We present a strategy for designing an α-β-η-θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α-β filter. Moreover, we apply an α-β-η-θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α-β-η-θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target's acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.

This paper describes the application of micro-Doppler radar (MDR) to gait classification based on fall risk-related differences using deep learning and gait parameter-based approaches. Two classification problems were considered in this study: elderly non-fallers and multiple fallers were classified to investigate the detection of fall risk-related gait differences, and middle-aged (50s) and elderly (70s) adults were classified to detect aging-related gait differences. The MDR signal data of the participants were simulated using an open motion capture gait dataset. The classification results obtained using the deep learning and gait parameter-based approaches showed that the classification accuracy achieved using a support vector machine with the gait parameters extracted from the MDR signals was better than that resulting from the deep learning of spectrogram (time-velocity distribution) images of the MDR signals for both classification problems. The gait parameter-based approach achieved the classification rates of 79 % for faller/non-faller classification and 82 % for 50s/70s classification, whereas the corresponding accuracies were 73 % and 76 %, respectively, using the deep learning approach. These results reveal that the gait parameters extracted via MDR measurements include sufficient information on gait to detect individuals with a high risk of falls and the gait parameter-based approaches are thus effective for both classification problems.