In biomechanical joint-motion analyses, the continuous motion to be studied is often approximated by a sequence of finite displacements, and the Finite Helical Axis(FHA) or "screw axis" for each displacement is estimated from position measurements on a number of anatomical or artificial landmarks. When FHA parameters are directly determined from raw (noisy) displacement data, both the position and the direction of the FHA are ill-determined, in particular when the sequential displacement steps are small. This implies, that under certain conditions, the continuous pathways of joint motions cannot be adequately described. The purpose of the present experimental study is to investigate the applicability of smoothing (or filtering)techniques, in those cases where FHA parameters are ill-determined. Two different quintic-spline smoothing methods were used to analyze the motion data obtained with Roentgenstereophotogrammetry in two experiments. One concerning carpal motions in a wrist-joint specimen, and one relative to a kinematic laboratory model, in which the axis positions are a priori known. The smoothed and nonsmoothed FHA parameter errors were compared. The influences of the number of samples and the size of the sampling interval (displacement step) were investigated, as were the effects of equidistant and nonequidistant sampling conditions and noise invariance
DOCUMENT
Here, we delve into Demand Forecasting via Machine Learning, dissecting how to predict future demand using time-sensitive data. Westveer highlights key forecasting models, from the basic Simple Exponential Smoothing to the advanced SARIMA, applied to an electricity production dataset. The session, encapsulating the essence of data-driven forecasting, culminates in a compelling three-year predictive outlook, illustrating the transformative potential of machine learning in strategic planning and decision-making.
VIDEO
The American company Amazon has made headlines several times for monitoring its workers in warehouses across Europe and beyond.1 What is new is that a national data protection authority has recently issued a substantial fine of €32 million to the e-commerce giant for breaching several provisions of the General Data Protection Regulation (gdpr) with its surveillance practices. On 27 December 2023, the Commission nationale de l’informatique et des libertés (cnil)—the French Data Protection Authority—determined that Amazon France Logistique infringed on, among others, Articles 6(1)(f) (principle of lawfulness) and 5(1)(c) (data minimization) gdpr by processing some of workers’ data collected by handheld scanner in the distribution centers of Lauwin-Planque and Montélimar.2 Scanners enable employees to perform direct tasks such as picking and scanning items while continuously collecting data on quality of work, productivity, and periods of inactivity.3 According to the company, this data processing is necessary for various purposes, including quality and safety in warehouse management, employee coaching and performance evaluation, and work planning.4 The cnil’s decision centers on data protection law, but its implications reach far beyond into workers’ fundamental right to health and safety at work. As noted in legal literature and policy documents, digital surveillance practices can have a significant impact on workers’ mental health and overall well-being.5 This commentary examines the cnil’s decision through the lens of European occupational health and safety (EU ohs). Its scope is limited to how the French authority has interpreted the data protection principle of lawfulness taking into account the impact of some of Amazon’s monitoring practices on workers’ fundamental right to health and safety.
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