Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 201

Context-Aware Android Applications through Transportation Mode Detection Techniques

In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility-aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data (accelerometer/gyroscope) with a novel meta-classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS-based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third-part mobile applications to access context-aware information in a transparent way

Laparoscopic mesogastrium excision for gastric cancer. Only the beginning

Background: Surgery, with the aid of chemotherapy and radiotherapy, is the only curative chance for gastric cancer. Unfortunately, gastric cancer had an elevated recurrence rate, primarily locally. Mesogastrium excision (MGE) during D2 lymphadenectomy has the aim to remove all possible contaminated tissue around the stomach. Methods: PubMed, EMBASE, and the Web of Science (WOS) were systematically searched for MGE reports in gastric cancer up to March 2020. The outcome reported were the number of lymph nodes retrieved, operative time (OT), overall morbidity, intra- and postoperative complications, conversion rate, and length of hospital stay. Results: A total of six studies, including 518 patients, were considered eligible for this analysis. All the studies reported laparoscopic cases. The mean number of lymph nodes retrieved was 36.7 ± 10.1. Mean OT was 240.7 ± 10.1 minutes. One case of conversion is reported. Overall morbidity was 6%. Medium estimated blood loss was 50.2 ± 39.6 mL. Overall length of stay was 10.7 ± 0.7 days. Mean follow-up was 11 ± 1.4 months. Conclusions: Only few studies evaluated this item, and according to the available evidence, MGE is a feasible technique that could be performed, also laparoscopically, in all surgical resections for gastric cancer with curative intent. Further studies are essential to establish the clear indication of this invasive procedure

Evidence on postoperative abdominal binding. A systematic review with meta-analysis of randomized controlled trials

Background: Midline laparotomy is an unavoidable approach to many surgical procedures. Many surgeons prescript the use of postoperative abdominal binder during the first mobilization after surgery. The use and the cost effective of this device is still debated by many surgeons. Methods: PubMed, EMBASE and the CENTRAL were systematically searched for randomized controlled trials (RCT) comparing patients who wore abdominal binder ("binder") and patient who did not wear any abdominal binder ("non-binder") up to March 2020. The primary outcomes measured in the comparison were postoperative pain, pulmonary functions, the entity of physical activity, the comfort. A meta-analysis of relevant studies was performed using RevMan 5.3. Results: wearing an abdominal binder after midline laparotomy seems to reduce postoperative pain on first and third postoperative day, to improve the physical activity on third postoperative day, and not affect pulmonary functions. Generally, an elastic abdominal binder is well tolerated during postoperative. Conclusions: the use of elastic abdominal binder permits a comfortable early postoperative mobilization reducing pain, increases physical activity and seems to not affect pulmonary functions

Autonomic Faulty Node Replacement in UAV-Assisted Wireless Sensor Networks: a Test-bed

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows

Relativistic Digital Twin: Bringing the IoT to the Future

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario.Comment: 17 pages, 10 figures, 4 tables, 6 listing

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

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To Sense or to Transmit: A Learning-Based Spectrum Management Scheme for Cognitive Radiomesh Networks

Abstract—Wireless mesh networks, composed of interconnected clusters of mesh router (MR) and multiple associated mesh clients (MCs), may use cognitive radio equipped transceivers, allowing them to choose licensed frequencies for high bandwidth communication. However, the protection of the licensed users in these bands is a key constraint. In this paper, we propose a reinforcement learning based approach that allows each mesh cluster to independently decide the operative channel, the durations for spectrum sensing, the time of switching, and the duration for which the data transmission happens. The contributions made in this paper are threefold. First, based on accumulated rewards for a channel mapped to the link transmission delays, and the estimated licensed user activity, the MRs assign a weight to each of the channels, thereby selecting the channel with highest performance for MCs operations. Second, our algorithm allows dynamic selection of the sensing time interval that optimizes the link throughput. Third, by cooperative sharing, we allow the MRs to share their channel table information, thus allowing a more accurate learning model. Simulations results reveal significant improvement over classical schemes which have pre-set sensing and transmission durations in the absence of learning. I

Total Versus Completion Thyroidectomy: A Multidimensional Evaluation of Long-Term Vocal Alterations.

Background: Total thyroidectomy (TT) and completion thyroidectomy (CT) are two common surgical operations that are frequently followed by vocal symptoms despite preservation of the recurrent laryngeal nerve (RLN) and of the external branch of superior laryngeal nerve (EBSLN). The aim of this study was to analyze vocal alterations through endoscopic findings, videolaryngostroboscopy (VLS), acoustic vocal parameters and impact on patients' quality of life after surgery in the absence of laryngeal nerve injury. Methods: We enrolled 198 patients who underwent thyroidectomy by the same surgeon. One hundred twenty-six patients underwent TT (group TT) while 72 underwent CT (group CT). All patients underwent preoperative VLS and Voice Handicap Index (VHI) assessment and postoperative VHI, VLS and Acoustic Voice Analysis with Multidimensional Voice Program Analysis 12 to 18 months after surgery. Results: We observed a statistically significant higher rate of EBSLN injury in CT compared to TT. Even in the absence of RLN and EBSLN injury, patients who underwent TT and CT presented slightly worse acoustic vocal parameters and VHI scores compared to healthy controls. Interestingly, some acoustic vocal parameters and VHI scores were significantly worse in group CT compared to group TT. Conclusions: The higher rate of EBSLN injury in CT rather than in TT suggests a higher surgical risk in CT. The vocal parameters of loudness and self-perception of voice were significantly worse after CT, suggesting a larger trauma in patients' vocal outcome in CT if compared to TT, although these alterations were not reported as psychologically limiting daily life of patients. Nevertheless, the existence of multiple factors contributing to vocal alterations after thyroidectomy highlight the importance of a routine comprehensive functional voice analysis before and after surgery