Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty.

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 a 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 4.5 cm/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 consistency (ICC(3,k) = 0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.

Detecting mitoses in time-lapse images of embryonic epithelia using intensity analysis.

Although the frequency and orientation of mitoses can significantly affect the mechanics of early embryo development, these data have not been available due to a shortage of suitable automated techniques. Fluorescence imaging, though popular, requires biochemical intervention and is not always possible or desirable. Here, a new technique that takes advantage of a localized intensity change that occurs in bright field images is used to identify mitoses. The algorithm involves mapping a deformable, sub-cellular triangular mesh from one time-lapse image to the next so that corresponding regions can be identified. Triangles in the mesh that undergo darkening of a sufficient degree over a period consistent with mitosis are flagged. Mitoses are assumed to occur along the short axis of elliptical areas fit to suitably sized clusters of flagged triangles. The algorithm is less complex than previous approaches and it has strong discrimination characteristics. When applied to 15 image sets from neurulation-stage axolotl (Ambystoma mexicanum) embryos, it was able to correctly detect 86% of the manually identified mitoses, had less than 5% false positives and produced average angular errors of only 15 degrees . The new algorithm is simpler to implement than those previously available, is substantially more accurate, and provides data that is important for understanding the mechanics of morphogenetic movements.

Detection of mitoses in embryonic epithelia using motion field analysis.

Although computer simulations indicate that mitosis may be important to the mechanics of morphogenetic movements, algorithms to identify mitoses in bright field images of embryonic epithelia have not previously been available. Here, the authors present an algorithm that identifies mitoses and their orientations based on the motion field between successive images. Within this motion field, the algorithm seeks 'mitosis motion field prototypes' characterised by convergent motion in one direction and divergent motion in the orthogonal direction, the local motions produced by the division process. The algorithm uses image processing, vector field analyses and pattern recognition to identify occurrences of this prototype and to determine its orientation. When applied to time-lapse images of gastrulation and neurulation-stage amphibian (Ambystoma mexicanum) embryos, the algorithm achieves identification accuracies of 68 and 67%, respectively and angular accuracies of the order of 30 degrees , values sufficient to assess the role of mitosis in these developmental processes.