Abstract
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

Abstract
Previous work from our team, has proposed a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes, leading to the creation of a proof-of-concept system integrated in a toilet seat. However, for this approach to be industrially feasible, further optimization is needed, in particular in what concerns electrode materials compatible with injection moulding processes. In this paper we explore the use of different types of conductive materials as electrodes, aiming at industrial-scale production of a toilet seat capable of recording ECG data, without the need for bodyworn devices. In addition, the effect of cleaning agents applied to the materials over time. Our approach has been evaluated comparatively with a gold standard device, for a population of 15 healthy subjects. While some of the materials did not allow adequate signal acquisition in all users, one electrically conductive compound showed the best results as per heart rate and ECG waveform morphology analysis. For the best performing compound we were able to acquire signals in 100% of the sessions, with an average heart rate deviation between the reference and experimental systems of -3.67 +/- 5.05 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.99.

Abstract
This study presents non-invasive subject specific analysis using innovative tools from dynamic systems theory and image processing for sagittal plane anatomical marker tracking and digital filtering for detection of normalized phase differences of lower limb joint angular displacement and angular velocity coordination during long and short countermovement (CM) and muscle stretch-shortening cycle. Applied metrics captured at low-dimensional level (one variable - the phase) differences of CM neuromuscular control of lower limb joint coordination with greater dissimilarity between long and short CM, whereas no CM condition shares higher phase coordination at the hip, knee, ankle.

Abstract
Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.

Abstract
Inertial sensors are widely used to measure human movement. Although inertial sensors have been successfully applied to exergaming in the past, the problem of detecting foot motions to interact with stepping exergames is still largely understudied. In this work, we developed a new method to detect and classify step directions relying on inertial sensor data captured by two shoe-mounted inertial sensors. Drawing on previous results, we developed a single multiclass classifier to distinguish front, back, side, and center steps originating from any of these positions. Since some of these steps exhibit similar displacement patterns, the previous step position was also considered as an input to the classifier. The method was tested on a group of young and older adults, achieving an accuracy of 93.1%. Performance remained consistent throughout the acquisition time due to the introduction of a novel calibration approach designed to handle sensor orientation drift over time. This study provided the first insights into the potential of inertial sensors to detect the foot motions required to interact with stepping exergames. Experimental results support their application in a real scenario.

Abstract
This study presents and applies cross-correlation analysis of lower limb joint angular kinematic signal for coordination assessment during impulse phase of standard Maximum Vertical Jump (MVJ). Standard MVJ has been applied for noninvasive subject specific analysis at Counter Movement Jump (CMJ) and Drop Jump (DJ) with long and short Counter Movement (CM) for comparison with Squat Jump (SJ) and no CM. Long and short CM are intrinsically related to the underlying mechanisms of muscle Stretch–Shortening Cycle (SSC) with the integration of muscular action and synergistic joint coordination an open issue on the contribution for the task to perform. Lower limb joint coordination has thus received increasing research interest as an external observed result of the neuromuscular control mechanisms and the hypotheses of training and repetition contribution for stereotyped coordination with the need of objective tools considering entire time series while avoiding subjective point selection for coordination assessment. The results of maximum cross-correlation (CCr max) and corresponding time delay (t) from entire time series of the Hip (H), the Knee (K) and the Ankle (A) joints angular displacement (?), angular velocities (?) and angular accelerations (a) during a fifty-four trial sample with long, short and no CM performed by a group of healthy untrained subjects conduced to detection of maximum synchrony with null t of the lower limb ? at different conditions, whereas ? and a CCr led to detection of different time delay for CCr max with dominant negative t values corresponding to a proximal–distal coordination sequence at H–K-A. These results point thus for the need to consider entire time series of joint ? and a in addition to joint ? to detect different joint coordination and avoid subjective point selection for coordination assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.