Evaluating Human Activity and Usage Patterns of Appliances with Smart Meters

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.Population ageing is becoming a key issue for most western countries, due to the challenges that it poses to the sustainability of future healthcare systems. In this context, many proposals and development are emerging trying to enhance the independent living of elderly and cognitive impaired people at their own homes. For that purpose, the massive deployment of smart meter at houses and buildings, initially focused on improving the energy management, has become a useful tool to provide the society with a variety of services and applications that can be employed for independent living. This work proposes the use of a commercial smart meter that delivers the disaggregated consumption per appliance every hour. This device has been installed on a test house during a training period of two months, in order to infer the behavior routines in the usage of the microwave. After the training, every new day can be compared to the obtained usage pattern of that appliance, in order to launch a notification when the day routine significantly differs. Similarly, since the use of the microwave is related to cooking, activities such as breakfast, lunch or dinner, may also be monitored and/or compared to a trained pattern. The proposal has been validated preliminary with experimental data coming from the aforementioned household.Agencia Estatal de InvestigaciónUniversidad de Alcal

Sensitive and Makeable Computational Materials for the Creation of Smart Everyday Objects

The vision of computational materials is to create smart everyday objects using the materi- als that have sensing and computational capabilities embedded into them. However, today’s development of computational materials is limited because its interfaces (i.e. sensors) are unable to support wide ranges of human interactions , and withstand the fabrication meth- ods of everyday objects (e.g. cutting and assembling). These barriers hinder citizens from creating smart every day objects using computational materials on a large scale. To overcome the barriers, this dissertation presents the approaches to develop compu- tational materials to be 1) sensitive to a wide variety of user interactions, including explicit interactions (e.g. user inputs) and implicit interactions (e.g. user contexts), and 2) makeable against a wide range of fabrication operations, such cutting and assembling. I exemplify the approaches through five research projects on two common materials, textile and wood. For each project, I explore how a material interface can be made to sense user inputs or activities, and how it can be optimized to balance sensitivity and fabrication complexity. I discuss the sensing algorithms and machine learning model to interpret the sensor data as high-level abstraction and interaction. I show the practical applications of developed computational materials. I demonstrate the evaluation study to validate their performance and robustness. In the end of this dissertation, I summarize the contributions of my thesis and discuss future directions for the vision of computational materials

5G-enabled contactless multi-user presence and activity detection for independent assisted living

Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Advanced photonic and electronic systems WILGA 2018

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers around 400 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2018 was the XLII edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2018 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445. WILGA 2018 works were published in Proc. SPIE vol.10808

Recognizing Activities of Daily Living of People with Parkinson's

Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2022Parkinson's disease is a common neurodegenerative disease that affects a large part of the world's population. This disease involves a lot of symptoms, however the most prevalent is the change in the patient's movements or even the loss of functionality. There is no treatment, however it exists medication that relieves and reduces the symptoms for a period. A Parkinson’s patient needs to be watched by clinicians to understand if the medication is working correctly and to analyse the disease progression. The current way of doing this evaluation is at clinics where the patient needs to go to the clinic or to live there. With this into consideration it was requested a monitoring system of activities of daily living for Parkinson’s patient. The monitoring system consists in a mobile application in an Android smartphone serving as a diary for the patient of clinician to record the activities done at that moment. With this application, the patient needs to wear an accelerometer in the wrist to gather the acceleration in the 3-axis. The application besides the monitoring function, it gives the ability to the clinician to schedule lists of activities for the patient to do during the day, allowing the clinician to have some control. We carried out a study with 10 healthy participants which used the monitorization system for 3 days each. The patient would worn the accelerometer and record the activities that they would do throughout the day, was asked a minimum of 5 activities per day. Alongside this recording it was schedule 1 list of activities to be carried out each day, this list only had motor activities such as walk, sit down, and stand up. At the end of each participant study, it was made a questionnaire with standard usability questions and an interview that helped us understand if the system was reliable or not

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Algorithms leveraging smartphone sensing for analyzing explosion events

The increasing frequency of explosive disasters throughout the world in recent years have created a clear need for the systems to monitor for them continuously to improve the post-disaster emergency events such as rescue and recovery operations. Disasters both man-made and natural are unfortunate and not preferred, however monitoring them may be a lifesaving phenomenon in emergency scenarios. Dedicated sensors deployed in the public places and their associated networks to monitor such events may be inadequate and must be complemented for making the monitoring more pervasive and effective. In the recent past, modern smartphones with significant processing, networking and storage capabilities have become a rich source of mobile infrastructure empowering participatory sensing to address many problems in the area of pervasive computing. In the work presented in this dissertation, smartphone sensed data during disastrous scenarios is extensively studied, analyzed and algorithms were built for participatory sensing to address the problems, specifically in the context of Explosion -- Events which are of interest to the current study. This work presents description of the systems for assisting people by detecting, ranging and estimating intensity of the explosion events leveraging multi-modal smartphone sensors. This work also presents various challenges and opportunities in utilizing the capabilities of the sensors in smartphone for building such systems along with practical applications, limitations and future directions --Abstract, page iii