Alberto Belli

A High Reliability Wearable Device for Elderly Fall Detection

Falls are critical events among elderly people that requires timely rescue. In this paper, we propose a fall detection system consisting of an inertial unit that includes triaxial accelerometer, gyroscope, and magnetometer with efficient data fusion and fall detection algorithms. Starting from the raw data, the implemented orientation filter provides the correct orientation of the subject in terms of yaw, pitch, and roll angles. The system is tested according to experimental protocols, engaging volunteers who performed simulated falls, simulated falls with recovery, and activities of daily living. By placing our wearable sensor on the waist of the subject, the unit is able to achieve fall detection performance above those of similar systems proposed in literature. The results obtained through commonly adopted protocols show excellent accuracy, sensitivity and specificity, improving the results of other techniques proposed in the literature.

An android-based heart monitoring system for the elderly and for patients with heart disease

The current trend in health monitoring systems is to move from the hospital to portable personal devices. This work shows how consumer devices like heart rate monitors can be used not only for applications in sports, but also for medical research and diagnostic purposes. The goal pursued by our group was to develop a simple, accurate, and inexpensive system that would use a few pieces of data acquired by the heart rate monitor and process them on a smartphone to (i) provide detailed test reports about the users health state; (ii) store report records; (iii) generate emergency calls or SMSs; and (iv) connect to a remote telemedicine portal to relay the data to an online database. The system developed by our team uses sophisticated algorithms to detect stress states, detect and classify arrhythmia events, and calculate energy consumption. It is suitable for use by elderly subjects and by patients with heart disease (e.g., those recovering from myocardial infarction) or neurological conditions such as Parkinsons disease. Easy, immediate, and economical remote health control can therefore be achieved without the need for expensive hospital equipment, using only portable consumer devices.

A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor

Falls and their consequences are among the major health care problems affecting functional mobility and quality of life of elderly people. Even for people living independently, falls are common occurrences. In this paper, we present a waist-mounted device useful to detect possible falls in elderly people. Through data coming from a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer, and a barometer sensor integrated into our device, we are able to obtain a highly accurate estimation about posture and altitude of the subject. By means of such information, we have developed an extremely efficient system for fall detection, reaching 100% of sensitivity in commonly adopted testing protocols. In particular, the algorithm was tested according to three different experimental protocols, where volunteers performed several scenarios, including various types of falls, falls with recovery, and daily living activities frequent in the elderly. Results show that the proper combined use of the four sensors and efficient data fusion algorithms allow to achieve noticeable better performances to those obtained with similar systems proposed in the literature.

Interoperability issues among smart home technological frameworks

Population aging may be seen both as a human success story, the triumph of public health, medical advancements and economic development over diseases and injures, and as one of the most challenging phenomena that society faces in this century. Assistive technology in all its possible implementations (from Telemedicine to Ambient Assisted Living, and Ambient Intelligence) represents an emerging answer to the needs of the new generation of older adults whose desire is to live longer with a higher quality of life. Objective of this paper is to present the results of a public financed action for the development and implementation of an “integration platform” for Ambient Assisted Living that includes features of home automation (energy management, safety, comfort, etc.) and introduces “smart objects”, to monitor activities of daily living and detect any abnormal behavior that may represent a danger, or highlight symptoms of some incipient disease.

A real-time system to aid clinical classification and quantification of tremor in Parkinson's disease

The availability of an objective clinical evaluation in the diagnosis and monitoring of parkinsons disease is a primary importance objective in neurology. Furthermore, in many patients next to resting tremor typical of the disease are also found other types of tremor as kinetic and postural tremor so making the diagnosis difficult. The ability to classify the different types of tremor specific for each patient through an examination of the instrumental, non-invasive and very simple and fast is a great tool to aid the clinical diagnosis of the disease. Our system meets the above requirements. It consists of an inertial sensor that allows the acquisition of the quantities of interest, and by a series of algorithms able to provide an objective and quantitative assessment of the type and severity of tremor in patients with Parkinsons disease. The availability of an objective report on the severity of the disorder developed according to a strict correlation with the valuation provided by the UPDRS scale is a good starting point towards the personalization of care as well as being a useful tool in the analysis of the course of the disease.

An accurate device for real-time altitude estimation using data fusion algorithms

This paper presents an accurate system to estimate the altitude of a rigid body by fusing data from four low-cost sensors such as an accelerometer, a gyroscope, a magnetometer and an altimeter. Usually a MEMS altimeter barometric sensor allows to obtain the altitude signal from measures of atmospheric pressure and temperature but these measures are affected by noise that causes a significant error in the calculated altitude values. In order to get an accurate estimation of the altitude, in this work a complementary filter is used to fuse the raw signal of the altitude obtained from barometer sensor and vertical displacement signal calculated through a data fusion algorithm applied to the signals of the other three sensors. In order to evaluate the performance in human activity monitoring applications, the proposed device has been tested and compared with the system that currently presents the better performance for the same technology according to its experimental protocols. The results show that our device exceeds the performance provided by the currently systems reported in literature.

Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices

Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects’ normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject’s lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

SVM-based fall detection method for elderly people using Android low-cost smartphones

Nowadays society is moving to a scenery where autonomous elderly live alone in their houses. An automatic remote monitoring system using wearable and ambient sensors is becoming even more important, and is a challenge for the future in WSNs, AAL, and Home Automation areas. Relating to this, one of the most critical events for the safety and the health of the elderly is the fall. Lot of methods, applications, and stand-alone devices have been presented so far. This work proposes a novel method based on the Support Vector Machine technique and addressed to Android low-cost smartphones. Our method starts from data acquired from accelerometer and magnetometer, now available in all the low-end devices, and uses a set of features extracted from a processing of the two signals. After an initial training, the classification of fall events and non-fall events is performed by the Support Vector Machine algorithm. Since we have decided to use the smartphone as monitoring device, the use of other invasive wearable sensors is avoided, and the user have simply to hold the phone on his pocket. Moreover, we can use the cellular network for the eventual sending of notifications and alerts to relatives in case of falls. Actually, our tests show a good performance with a sensitivity of 99.3% and a specificity of 96%.

Binders alternative to Portland cement and waste management for sustainable construction—part 1:

This review presents “a state of the art” report on sustainability in construction materials. The authors propose different solutions to make the concrete industry more environmentally friendly in order to reduce greenhouse gases emissions and consumption of non-renewable resources. Part 1—the present paper—focuses on the use of binders alternative to Portland cement, including sulfoaluminate cements, alkali-activated materials, and geopolymers. Part 2 will be dedicated to traditional Portland-free binders and waste management and recycling in mortar and concrete production.

Real-time apnea detection using pressure sensor and tri-axial accelerometer

Respiratory disorders, if diagnosed late and untreated, may cause the advancement of many pathologies especially pertaining the cardiovascular system. This study proposes a method for a fast and certain detection of apnea events. For this purpose we used a commercial device that contains a pressure sensor helpful for the measurement of breath and a tri-axial accelerometer necessary to improve the detection reliability. There are numerous commercial devices able to detect breathing, but the totality of them is oriented to sport activity monitoring and so calibrated on the upper thresholds of respiratory rate. These devices are therefore not directly used as biomedical devices specific for the detection of sleep apneas or as life-saving devices in the case of “voluntary” apneas that occur in patients with severe neurological or pathological disorders. Then, we have developed complex algorithms to process the signals in real-time for the detection of apnea events with a maximum delay of 10 s, a sensitivity of 99%, and a specificity of 100%. This paper shows how an inexpensive approach is possible to control dependably the occurrence of apneas, avoiding hospitalization and the use of complex, invasive, and expensive devices.