DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultralow-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstrating the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-cost GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-server approach over 4G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution

Leveraging Model Fusion for Improved License Plate Recognition

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, there has been a noticeable lack of studies exploring the potential improvements in results by fusing the outputs from multiple recognition models. This research aims to fill this gap by investigating the combination of up to 12 different models using straightforward approaches, such as selecting the most confident prediction or employing majority vote-based strategies. Our experiments encompass a wide range of datasets, revealing substantial benefits of fusion approaches in both intra- and cross-dataset setups. Essentially, fusing multiple models reduces considerably the likelihood of obtaining subpar performance on a particular dataset/scenario. We also found that combining models based on their speed is an appealing approach. Specifically, for applications where the recognition task can tolerate some additional time, though not excessively, an effective strategy is to combine 4-6 models. These models may not be the most accurate individually, but their fusion strikes an optimal balance between accuracy and speed.Comment: Accepted for presentation at the Iberoamerican Congress on Pattern Recognition (CIARP) 202

Characterization of the stimulation output of four devices for focal muscle vibration

Different devices for mechano-acoustic muscle vibration became available on the market in the last ten years. Although the use of these vibrators is increasing in research and clinical settings, the features of their stimulation output were never described in literature. In this study we aimed to quantify and compare the stimulation output of the four most widespread pneumatic devices for focal muscle vibration available on the market. A piezoelectric pressure sensor was used to measure the pressure profile generated by the four selected devices in the following experimental conditions: i) measurement of the output changes associated with variations of the stimulation amplitude for three stimulation frequencies (100 Hz, 200 Hz, and 300 Hz); ii) measurement of the output changes during a 20-min long stimulation at constant frequency (300 Hz) and amplitude; iii) measurement of the output changes associated with the progressive activation of all stimulation channels at constant frequency (200 Hz) for different amplitudes. The maximum peak-to-peak amplitudes of the pressure waves were in the range 102 mbar - 369 mbar (below the maximum values declared by the different manufacturers). The shape of the pressure waves generated by the four devices was quasi-sinusoidal and asymmetric with respect to the atmospheric pressure. All output features had a remarkable intra- and inter-device variability. Further studies are required to support the technological improvement of the currently available devices and to focus the issues of vibration effectiveness, limitations, proper protocols, modalities of its application and assessment in neuromuscular training and rehabilitation

DXA-Based Detection of Low Muscle Mass Using the Total Body Muscularity Assessment Index (TB-MAXI): A New Index with Cutoff Values from the NHANES 1999–2004

The aims of this study were to investigate age-related changes in total body skeletal muscle mass (TBSMM) and the between-limb asymmetry in lean mass in a large sample of adults. Demographic, anthropometric, and DXA-derived data of National Health and Nutrition Examination Survey participants were considered. The sample included 10,014 participants of two ethnic groups (Caucasians and African Americans). The age-related decline of TBSMM absolute values was between 5% and 6% per decade in males and between 4.5% and 5.0% per decade in females. The adjustment of TBSMM for body surface area (TB-MAXI) showed that muscle mass peaked in the second decade and decreased progressively during the subsequent decades. The following thresholds were identified to distinguish between low and normal TB-MAXI: (i) 10.0 kg/m2 and 11.0 kg/m2 in Caucasian and African American females; and (ii) 12.5 kg/m2 and 14.5 kg/m2 in Caucasian and African American males. The lean asymmetry indices were higher for the lower limbs compared with the upper limbs and were higher for males compared with females. In conclusion, the present study proposes the TB-MAXI and lean asymmetry index, which can be used (and included in DXA reports) as clinically relevant markers for muscle amount and lean distribution

Architectural changes in superficial and deep compartments of the tibialis anterior during electrical stimulation over different sites

Electrical stimulation is widely used in rehabilitation to prevent muscle weakness and to assist the functional recovery of neural deficits. Its application is however limited by the rapid development of muscle fatigue due to the non-physiological motor unit (MU) recruitment. This issue can be mitigated by interleaving muscle belly (mStim) and nerve stimulation (nStim) to distribute the temporal recruitment among different MU groups. To be effective, this approach requires the two stimulation modalities to activate minimally-overlapped groups of MUs. In this manuscript, we investigated spatial differences between mStim and nStim MU recruitment through the study of architectural changes of superficial and deep compartments of tibialis anterior (TA). We used ultrasound imaging to measure variations in muscle thickness, pennation angle, and fiber length during mStim, nStim, and voluntary (Vol) contractions at 15% and 25% of the maximal force. For both contraction levels, architectural changes induced by nStim in the deep and superficial compartments were similar to those observed during Vol. Instead, during mStim superficial fascicles underwent a greater change compared to those observed during nStim and Vol, both in absolute magnitude and in their relative differences between compartments. These observations suggest that nStim results in a distributed MU recruitment over the entire muscle volume, similarly to Vol, whereas mStim preferentially activates the superficial muscle layer. The diversity between spatial recruitment of nStim and mStim suggests the involvement of different MU populations, which justifies strategies based on interleaved nerve/muscle stimulation to reduce muscle fatigue during electrically-induced contractions of TA