Alesandro Puiatti
@supsi.ch
University of Applied Sciences and Arts of Southern Switzerland
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ariyamehr Rezaei, Alessandro Mascheroni, Michael C. Stevens, Reza Argha, Michela Papandrea, Alessandro Puiatti, and Nigel H. Lovell
Institute of Electrical and Electronics Engineers (IEEE)
Flavio Raschellà, Stefano Scafa, Alessandro Puiatti, Eduardo Martin Moraud, and Pietro‐Luca Ratti
Wiley
OBJECTIVES
REM sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome exam requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings.
METHODS
Twenty-six Parkinson's patients underwent two-week home wrist actigraphy, followed by two in-lab evaluations. Patients were classified as RBD vs. non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-lab and at-home recordings. Performance was further validated in a control group of non-PD patients with other sleep conditions.
RESULTS
To characterize RBD, actigraphic features extracted from both (i) individual movement episodes and (ii) global nocturnal activity were critical. RBD patients were more active overall, and exhibited movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9±8.16% during in-clinic tests. When validated on home recordings in Parkinson's patients, accuracy reached 100% over a two-week window, and was 94.4% in non-PD control patients. Features showed robustness across tests and conditions.
INTERPRETATIONS
These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. This article is protected by copyright. All rights reserved.
Nushara Wedasingha, Pradeepa Samarasinghe, Lasantha Senevirathna, Michela Papandrea, Alessandro Puiatti, and Debbie Rankin
Springer Science and Business Media LLC
Mauro Citraro, Cristina Carcano-Monti, Simone Pellegrini, Alessandro Puiatti, and Lorenzo Sommaruga
IEEE
The birth of a student entrepreneurial idea needs a delivery room and a research methodology. It should be nourished to grow like a nursery takes care of its offspring for them to become healthy adults. The initial birth of a startup idea needs encouragement, assistance and protection. Entreprenursery's main goal is to bring to life ideas and to help them develop once discovered. In this respect, Entreprenursery, working as an accelerator of ideas, is built on stakeholders who take care of the student's inclination for entrepreneurship. Entreprenursery is characterized by an informal teaching approach that breaks down barriers, fosters involvement and creates an enjoyable working atmosphere within students, mentors, corporates and entrepreneurial members of the society. All of them are part of a quadruple helix model in which the main actors are students leading their startup ideas to innovation. A digital platform – pingel@p – manages the collection and helps all stakeholders to work together to reach a common goal: allowing startup ideas to grow in a safe but encouraging environment by taking care of the intellectual property (IP) as well. Providing IP protection gives students a nurtured feeling and a sense of ownership. In Entreprenursery, students are also confronted with companies expressing their business needs and challenges, allowing entrepreneurial students to take them up.
Mohsen Rezaei, Michael C. Stevens, Ahmadreza Argha, Alessandro Mascheroni, Alessandro Puiatti, and Nigel H. Lovell
Institute of Electrical and Electronics Engineers (IEEE)
Given the aging population, healthcare systems need to be established to deal with health issues such as injurious falls. Wearable devices can be used to detect falls. However, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this study, we developed an unobtrusive monitoring system using infrared technology to unobtrusively detect locations and recognize human activities such as sitting, standing, walking, lying, and falling. We prototyped a system consisting of two 24×32 thermal array sensors and collected data from healthy young volunteers performing ten different scenarios. A supervised deep learning (DL)-based approach classified activities and detected locations from images. The performance of the DL approach was also compared with the machine learning (ML)-based methods. In addition, we fused the data of two sensors and formed a stereo system, which resulted in better performance compared to a single sensor. Furthermore, to detect critical activities such as falling and lying on floor, we performed a binary classification in which one class was falling plus lying on floor and another class was all the remaining activities. Using the DL-based algorithm on the stereo dataset to recognize activities, overall average accuracy and F1-score were achieved as 97.6%, and 0.935, respectively. These scores for location detection were 97.3%, and 0.927, respectively. These scores for binary classification were 97.9%, and 0.945, respectively. Our results suggest the proposed system recognized human activities, detected locations, and detected critical activities namely falling and lying on floor accurately.
Yohann Thenaisie, Kyuhwa Lee, Charlotte Moerman, Stefano Scafa, Andrea Gálvez, Elvira Pirondini, Morgane Burri, Jimmy Ravier, Alessandro Puiatti, Ettore Accolla,et al.
American Association for the Advancement of Science (AAAS)
Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.
Nushara Wedasingha, Pradeepa Samarasinghe, Dharshika Singarathnam, Michela Papandrea, Alessandro Puiatti, and Lasantha Seneviratne
IEEE
This paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.
Nushara Wedasingha, Pradeepa Samarasinghe, Lasantha Seneviratne, Alessandro Puiatti, Michela Papandrea, and Dulangi Dhanayaka
IEEE
This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior.
Alessandro Mascheroni, Eun Kyoung Choe, Yuhan Luo, Michele Marazza, Clara Ferlito, Serena Caverzasio, Francesco Mezzanotte, Alain Kaelin-Lang, Francesca Faraci, Alessandro Puiatti,et al.
JMIR Publications Inc.
Background Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. Objective The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients’ homes, and as a remote tool for researchers to monitor patients and integrate and manage data. Methods An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. Results From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. Conclusions SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. Trial Registration ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396
Ariyamehr Mohsen Rezaei, Michael C. Stevens, Ahmadreza Argha, Alessandro Mascheroni, Alessandro Puiatti, and Nigel H. Lovell
IEEE
Human activity recognition has many potential applications. In an aged care facility, it is crucial to monitor elderly patients and assist them in the case of falls or other needs. Wearable devices can be used for such a purpose. However, most of them have been proven to be obtrusive, and patients reluctate or forget to wear them. In this study, we used infrared technology to recognize certain human activities including sitting, standing, walking, laying in bed, laying down, and falling. We evaluated a system consisting of two 24×32 thermal array sensors. One infrared sensor was installed on side and another one was installed on the ceiling of an experimental room capturing the same scene. We chose side and overhead mounts to compare the performance of classifiers. We used our prototypes to collect data from healthy young volunteers while performing eight different scenarios. After that, we converted data coming from the sensors into images and applied a supervised deep learning approach. The scene was captured by a visible camera and the video from the visible camera was used as the ground truth. The deep learning network consisted of a convolutional neural network which automatically extracted features from infrared images. Overall average F1-score of all classes for the side mount was 0.9044 and for the overhead mount was 0.8893. Overall average accuracy of all classes for the side mount was 96.65% and for the overhead mount was 95.77%. Our results suggested that our infrared-based method not only could unobtrusively recognize human activities but also was reasonably accurate.
Maurizio Capra, Stefano Sapienza, Paolo Motto Ros, Alessio Serrani, Maurizio Martina, Alessandro Puiatti, Paolo Bonato, and Danilo Demarchi
MDPI AG
Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.
Simone Sguazza, Alessandro Puiatti, Sandra Bernaschina, Francesca Faraci, Gianpaolo Ramelli, Vincenzo D’Apuzzo, Emmanuelle Rossini, and Michela Papandrea
Springer International Publishing
Luigi Fiorillo, Alessandro Puiatti, Michela Papandrea, Pietro-Luca Ratti, Paolo Favaro, Corinne Roth, Panagiotis Bargiotas, Claudio L. Bassetti, and Francesca D. Faraci
Elsevier BV
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algorithms have been applied to sleep scoring for many years. As a result, several software products offer nowadays automated or semi-automated scoring services. However, the vast majority of the sleep physicians do not use them. Very recently, thanks to the increased computational power, deep learning has also been employed with promising results. Machine learning algorithms can undoubtedly reach a high accuracy in specific situations, but there are many difficulties in their introduction in the daily routine. In this review, the latest approaches that are applying deep learning for facilitating and accelerating sleep scoring are thoroughly analyzed and compared with the state of the art methods. Then the obstacles in introducing automated sleep scoring in the clinical practice are examined. Deep learning algorithm capabilities of learning from a highly heterogeneous dataset, in terms both of human data and of scorers, are very promising and should be further investigated.
Pietro-Luca Ratti, Francesca Faraci, Sandra Hackethal, Alessandro Mascheroni, Clara Ferlito, Serena Caverzasio, Ninfa Amato, Eun Kyoung Choe, Yuhan Luo, Paulo-Edson Nunes-Ferreira,et al.
IOS Press
Background: Subjective symptoms, which are retrospectively assessed during clinical interviews in the office, may be influenced by patient recall in Parkinson’s disease (PD). Prospective collection of subjective data might be an effective tool to overcome this bias. Objective: We investigated the correspondence between prospectively and retrospectively assessed motor symptoms in PD. Methods: Forty-two consecutive patients (9 females, 67±9.8 years old) with mild to moderate PD reported their symptoms four times a day for two weeks, using the “SleepFit” application (app) for tablets. This app incorporates a new Visual Analogue Scale assessing global mobility (m-VAS), and the Scales for Outcome in Parkinson Assessment Diary Card (SCOPA-DC). At day 14, the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) parts II and IV questionnaires were completed at the hospital. Agreement (root mean square difference) and the tendency to under- or overestimate their symptoms by patients (relative difference after normalization) were calculated to compare prospectively vs. retrospectively collected information. Results: Although agreement was good for overall scores (m-VAS: 10.0%; SCOPA-DC: 18.3%), and for single motor symptoms (involuntary movements, hand dexterity, walking, changing position; each <20%), some individuals with more advanced disease, higher fatigue or worse sleep quality showed poor symptom recall in retrospect. Moreover, a subgroup of patients (16.7%) either over- or underestimated symptom severity. Conclusions: Regular, prospective monitoring of motor symptoms is suitable in PD patients. SleepFit might be a useful tool in routine practice to identify patients tending to under- or overestimate their symptoms, and for their follow-up.
Francesca D. Faraci, Michela Papandrea, Alessandro Puiatti, Stefania Agustoni, Sara Giulivi, Vincenzo DrApuzzo, Silvia Giordano, Flavio Righi, Olmo Barberis, Evelyne Thommen,et al.
IEEE
AutoPlay is the name of an innovative idea to promote healthy childhood. It aims at anticipating the diagnosis of autism spectrum disorders, neuro developmental disorders and social fragilities. AutoPlay exploits data collected during ludic activities and translates them into clinical and social information. In this paper we describe the concept, design and manufacturing of the first AutoPlay toys-kit prototype. We also present its very first application in a feasibility study. We demonstrated that it is possible to answer the need of capturing useful information related to infant (9–15 months) ludic activity, in an ecological environment. The toys-kit is the result of an inter-disciplinary collaboration, which has merged evenly clinical and technological perspectives and requirements. We managed to clarify what the available technologies can offer to pediatricians and social operators, and how it is be possible to measure the quality of toys manipulation.
Edoardo Bonizzoni, Alessandro Puiatti, Stefano Sapienza, Paolo Motto Ros, Danilo Demarchi, and Paolo Bonato
IEEE
This study presents preliminary results of a broader research on Home Robot monitoring for elder people. The final goal of the project is the development of a robot that works in synergy with an automatic fall detection device, reaching the patient and checking his condition in case of triggered alarm. This paper covers the initial steps necessary for the design of the tracking network which provides the machine with the subject's position, in particular the single node performance. The network is based on Ultra-Wide Band (UWB) wireless transceivers that in this study are the Decawave EVB1000 evaluation boards. Two types of analysis have been performed on the anchor: a Line of Sight (LOS) baseline accuracy and interference robustness. The results demonstrate that, for LOS distance estimation, to achieve a margin of error below 15 cm, the node has to be closer than 12 m to the target. If we remove the line of sight condition, introducing a subject walking straight between the two anchors, the error is spread in the order of 10 cm from the original baseline for a 10 m nodes distance recording. If the path is obstructed instead by a subject walking perpendicularly to the nodes instead leads to a different types of perturbations, with an absolute error below 13 cm.
Sunghoon Ivan Lee, Muzaffer Yalgin Ozsecen, Luca Della Toffola, Jean-Francois Daneault, Alessandro Puiatti, Shyamal Patel, and Paolo Bonato
IEEE
Motivated by a need for accurate assessment and monitoring of patients with knee osteoarthritis in an ambulatory setting, a wearable electrogoniometer composed of a knee angular sensor and a three-axis accelerometer placed on the thigh is developed. Accurate assessment of knee kinematics requires accurate detection of walking amongst dynamic, heterogeneous, and individualized activities of daily living. This paper investigates four different machine learning techniques for detecting occurrences of walking in uncontrolled environments based on a dataset collected from a total of 4 healthy subjects. Multi-class classifier (random forest) based detection method showed the best performance, which supports 90% precision and 75% recall. The in-depth analysis and interpretation of the results show that accurate decision boundaries are necessary between 1) fast walking and descending stairs, 2) slow walking and ascending stairs, as well as 3) slow walking and transitional activities. This work provides a systematic approach to detect occurrences of walking in uncontrolled living conditions, which can also be extended to other activities.
Oscar Mayora, Mads Frost, Bert Arnrich, Franz Gravenhorst, Agnes Grunerbl, Amir Muaremi, Venet Osmani, Alessandro Puiatti, Nina Reichwaldt, Corinna Scharnweber,et al.
IGI Global
This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a starting point for identifying important non-functional requirements involved in mobile health provisioning that are fundamental for the successful implementation of mobile health services in real life contexts.
Armando Rivero, Gianluca Costante, Edoardo Bonizzoni, Alessandro Puiatti, and Anna Förster
ACM
The Zigbee low-power communication standard has established itself as one of the most important wireless standards, enabling thousands of industrial and environmental monitoring applications. At the same time, Bluetooth and newly also Bluetooth Low Energy has captured the gadget and smartphone markets and currently enables various health and personal applications. The border between these two markets becomes thinner and applications would profit significantly from interconnecting these two standards and sharing the information obtained. We will demonstrate our custom designed device BLupZi, which interconnects the worlds of Bluetooth Low Energy and Zigbee. It can be configured to stream all data from one of the networks to the other or to filter particular packet types or source IDs. We will present two examples with two different types of Zigbee sensor nodes and a smartphone.
Alessandro Puiatti, Armando Rivero, Anna Forster, Andrea Bernaschina, Gian Carlo Dozio, Nicola Rizzo, Nunzio De Bitonti, and Andrea Cavicchioli
IEEE
In the health care domain up to the 70% of the revenues can be due to the staff workload costs, which are rerouted to health care insurances and patients. Therefore, it is of paramount importance to determine how much of these costs are really due to the patient care. Some hospitals adopted Real Time Location System solutions in order to optimize staff workload. However, even though these systems are able to localize the staff inside the hospital facilities, they are not intended for computing the time spent by a caregiver while taking care of a patient. Moreover, the localization accuracy of these systems is too low to address this problem. In this paper we present Proxy-Care, a novel patient care tracking system able to compute automatically the time spent by the caregivers, differentiated by doctors, nurses and caretakers, while they are taking care of individual patients. Our results show that with Proxy-Care we are able to localize the caregiver at room level with an accuracy of 98.5%, and to recognize the proximity between the caregiver and the patient, in rooms with two or more patients, with an accuracy of 92%.
D. Sanz, A. Barrientos, M. Garzón, C. Rossi, M. Mura, D. Puccinelli, A. Puiatti, M. Graziano, A. Medina, L. Mollinedo,et al.
Elsevier BV
Planetary surface exploration is an appealing application of wireless sensor networks that has been investigated in recent years by the space community, including the European Space Agency. The idea is to deploy a number of self-organizing sensor nodes forming a wireless networked architecture to provide a distributed instrument for the study and exploration of a planetary body. To explore this concept, ESA has funded the research project RF Wireless for Planetary Exploration (RF-WIPE), carried out by GMV, SUPSI and UPM. The purpose of RF-WIPE was to simulate and prototype a wireless sensor network in order to assess the potential and limitations of the technology for the purposes of planetary exploration.
In this paper, we illustrate the results of the work carried out within the context of RF-WIPE. Two test case scenarios have been investigated: a distributed sensor network-based instrument and networked planetary surface exploration. Each scenario is related to a particular network configuration. For such configurations, energy models and communication protocols have been developed, simulated, and validated both on laboratory tests and with outdoor field tests. Additionally, node deployment was investigated, and a deployment system based on a mobile robotics platform has been designed and tested.
Oscar Mayora, Bert Arnrich, Jakob Bardram, Carsten Dräger, Andrea Finke, Mads Frost, Silvia Giordano, Franz Gravenhorst, Agnes Grunerbl, Christian Haring,et al.
IEEE
This paper presents the lessons learnt on the design, development and evaluation of a pervasive computing-based system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a set of relevant checklist items in the development of innovative solutions for mental health treatment and in a broader way for future research on personal health systems.
Daniel Widmann, Katarina Balac, Antonio Vincenzo Taddeo, Mauro Prevostini, and Alessandro Puiatti
IEEE
An increased number of vehicular applications and services requires accurate distance measurements. Due to specific properties of radio waves propagation, it may not be effective to use ranging systems designed for other environments inside tunnels. In this paper we analysed the characteristics of time of flight based ranging for in-tunnel applications. Based on our analysis, we designed a vehicle localization system showing that the time of flight approach is a suitable, accurate and cost effective solution for this purpose. We designed and validated our solution by performing real experiments in a tunnel located in Lugano, Switzerland.
K. Zheng, Mo Li and H. Jiang
Springer Berlin Heidelberg
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2012, held in Beijing, China, Denmark, in December 2012. The revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a wide range of topics such as localization and tracking, search and discovery, classification and profiling, context awareness and architecture, location and activity recognition. The proceedings also include papers from the best paper session and the industry track, as well as poster and demo papers.