Background: Walking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist. Methods: The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups. Results: Several parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180 • turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups. Conclusion: Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand

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This paper considers the control design for a lowcost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is physically unable to breathe. First, it experimentally shows that for accurately tracking tidal volumes, the controller needs to be adapted to the individual patient and the different configurations, e.g., hardware or operation modes. Second, it proposes a set-point adaptation algorithm that uses sensor measurements of a flow meter to automatically adapt the controller to the setup at hand. Third, it experimentally shows that such an adaptive solution improves the performance of the ventilator for various setups. One objective of this paper is to increase awareness of the need for feedback control using sensor measurements in low-cost ventilator solutions in order to automatically adapt to the specific scenario.

A high-quality, low-cost ventilator, dubbed HEV, has been developed by the particle physics community working together with biomedical engineers and physicians around the world. The HEV design is suitable for use both in and out of hospital intensive care units, provides a variety of modes and is capable of supporting spontaneous breathing and supplying oxygen-enriched air. An external air supply can be combined with the unit for use in situations where compressed air is not readily available. HEV supports remote training and post market surveillance via a Web interface and data logging to complement standard touch screen operation, making it suitable for a wide range of geographical deployment. The HEV design places emphasis on the ventilation performance, especially the quality and accuracy of the pressure curves, reactivity of the trigger, measurement of delivered volume and control of oxygen mixing, delivering a global performance which will be applicable to ventilator needs beyond the COVID-19 pandemic. This article describes the conceptual design and presents the prototype units together with a performance evaluation.

Aiming to address clinical requirements subsequent to SARS-CoV-2-related pulmonary disease, multiple research groups and industry groups carried out intensive studies to develop pandemic ventilators (PDVs). In vitro testing to critically evaluate the specific performance of the developed apparatuses is an essential requirement. This study presents a test protocol which promotes a test-oriented, iterative, and agile assessment and consecutive development of such PDVs. It allows for fast identification of specific characteristics of each PDV in the individual test features. The test protocol includes an evaluation of the accuracy of control systems and instruments at changing parameters, the oxygen dynamics, and the response to trigger signals. The test environment is a mechanical lung, which allows reproducing various lung mechanics and to simulate active breathing cycles. A total of three PDVs that are under development were iteratively tested, with a Hamilton T1 as a reference. Continuous testing of the PDVs under development enables quick identification of critical application aspects that deserve further improved. Based on the present test protocol, the ventilators demonstrate a promising performance justifying continued development.