This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy.

This article describes an ambulatory gait event detection method for long-term monitoring of walking. Aminian et al. [2] have developed an automatic gait event detection algorithm based on shank-attached gyroscope signals. However, this algorithm has a drawback in that it is post-processed. We propose a modified algorithm which detects foot initial and end contact timings using the same concept as in [2], but in quasi real-time. The utilization of the knowledge on gait sequence and peak angular acceleration realizes the quasi real-time detection. Furthermore, to be practical, the algorithm has been developed to ensure the robustness of detection (i.e., without missing the gait events in various speed conditions). Validation of the algorithm using footswitches shows that the algorithm detected the end contacts earlier (-8 ms) and the initial contacts later (19 ms) than the footswitch-based method.

Although near-falls (or recoverable imbalances) are common episodes for many older adults, they have received a little attention and were not considered in the previous laboratory-based fall assessments. Hence, this paper addresses near-fall scenarios in addition to the typical falls and activities of daily living (ADLs). First, a novel vertical velocity-based pre-impact fall detection method using a wearable inertial sensor is proposed. Second, to investigate the effect of near-fall conditions on the detection performance and feasibility of the vertical velocity as a fall detection parameter, the detection performance of the proposed method (Method 1) is evaluated by comparing it to that of an acceleration-based method (Method 2) for the following two different discrimination cases: falls versus ADLs (i.e., excluding near-falls) and falls versus non-falls (i.e., including near-falls). Our experiment results show that both methods produce similar accuracies for the fall versus ADL detection case; however, Method 1 exhibits a much higher accuracy than Method 2 for the fall versus non-fall detection case. This result demonstrates the superiority of the vertical velocity over the peak acceleration as a fall detection parameter when the near-fall conditions are included in the non-fall category, in addition to its capability of detecting pre-impact falls.

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