ATLANE – Seite 7

Big Apple

By J.D. März 24, 2022

Es wurde Zeit wieder einmal die Segel zu hissen und neue Eindrücke zu sammeln. Diesmal im Trubel einer Großstadt. Vermutlich hat jeder seine Vorstellungen aus Filmen oder Erzählungen über New York. Geschichten neuerlicher Zeit als auch aus vergangenen Tagen ranken sich um diese Stadt, welche wie ein Nabel der Welt erscheint. Zeit sich selbst ein Bild zu machen. Da es zum Zeitpunkt aufgrund der Corona Lage und der erst wieder, für Touristen, geöffneten USA nicht…

.reisen

Kanada Loipe

By J.D. Februar 12, 2022

Eine schöne Loipe am Sylvensteinspeicher entlang der wilden Isar. Weitere Informationen über die Seite von Wallgau als auch der Schneebericht.

.piHacks

Update DeLock WLAN-Steckdose 11826/11827 – Tasmota 9

By J.D. Januar 15, 2022

Anleitung für ein Update der DeLock WLAN-Steckdose

.projekte

Homekit / ESP32 / BME680 Sensor

By J.D. Januar 9, 2022

Nach erfolgreichem Einsatz der „Homebridge“ für einige WLAN Steckdosen nun der Versuch der Integration von Sensoren. Sensor Der BME680 bietet eine Vielzahl von Parametern (Temperatur, VOC, IAQ etc.), ebenso existiert eine öffentliche Bibliothek zu Ansteuerung namens BSEC. HomeKit SDK Das HAP-R2 Protokoll das Apple für seine HomeKit Anwendung verwendet ist teilweiße öffentlich, sodass die Spezifikationen für das Protokoll auch in Opensource-Software nachgebildet werden konnte. Espressif selbst bietet bereits ein homekit-sdk an. Nach kurzer Suche findet…

.gekritzel

Cycle II: it’s not only your own horny cycle

By J.D. November 29, 2021

Abstract : Postural instability is a common complication in advanced Parkinson’s disease (PD) associated with recurrent falls and fall-related injuries. The test of retropulsion, consisting of a rapid balance perturbation by a pull in the backward direction, is regarded as the gold standard for evaluating postural instability in PD and is a key component of the neurological examination and clinical rating in PD (e.g., MDS-UPDRS). However, significant variability in test execution and interpretation contributes to a low intra- and inter-rater test reliability. Here, we explore the potential for objective, vision-based assessment of the pull test (vPull) using 3D pose tracking applied to single-sensor RGB-Depth recordings of clinical assessments. The initial results in a cohort of healthy individuals (n = 15) demonstrate overall excellent agreement of vPull-derived metrics with the gold standard marker-based motion capture. Subsequently, in a cohort of PD patients and controls (n = 15 each), we assessed the inter-rater reliability of vPull and analyzed PD-related impairments in postural response (including pull-to-step latency, number of steps, retropulsion angle). These quantitative metrics effectively distinguish healthy performance from and within varying degrees of postural impairment in PD. vPull shows promise for straightforward clinical implementation with the potential to enhance the sensitivity and specificity of postural instability assessment and fall risk prediction in PD. ... read on

Abstract : Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables the determination of temporal and spatial gait cycle … ... read on

Abstract : Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor’s efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear’s efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes. ... read on

Big Apple

Kanada Loipe

Update DeLock WLAN-Steckdose 11826/11827 – Tasmota 9

Homekit / ESP32 / BME680 Sensor

Cycle II: it’s not only your own horny cycle

Precision Balance Assessment in Parkinson’s Disease: Utilizing Vision-Based 3D Pose Tracking for Pull Test Analysis

Mobile spatiotemporal gait segmentation using an ear-worn motion sensor and deep learning

Human activity recognition in a free-living environment using an ear-worn motion sensor