Technological advances in manufacturing smart high-performances electronic devices, increasingly available at lower costs, nowadays allow one to improve users' quality of life in many application fields. In this work, the human-machine interaction obtained by using a next generation device (Myo armband) is analyzed and discussed, with a particular focus to healthcare applications such as upper-limb prostheses. An overview on application fields of the Myo armband and on the latest research works related to its use in prosthetic applications is presented; subsequently, the technical features and functionalities of this device are examined. Myo armband is a wearable device provided with eight electromyographic electrodes, a 9-axes inertial measurement unit and a transmission module. It sends the data related to the detected signals, via Bluetooth Low Energy technology, to other electronic devices which process them and act accordingly, depending on how they are programmed (in order to drive actuators or perform other specific functions). Applied to the prosthetic field, Myo armband allows one to overcome many issues related to the existing prostheses, representing a complete electronic platform that detects in real-time the main signals related to forearm activity (muscles activation and forearm movements in the three-dimensional space) and sends these data to the connected devices. Nowadays, several typologies of prostheses are available on the market; they can be mainly distinguished into low-cost prostheses, which are light and compact but allow for a limited number of movements, and high-end prostheses, which are much more complex and featured by high dexterity, but also heavy, bulky, difficult to control and very expensive. Finally, the Myo armband is an optimum candidate for prosthetic application (and many others) and offers an excellent lowcost solution for obtaining a reliable, easy to use system.

Taking the advantages offered by smart high-performance electronic devices, transradial prosthesis for upper-limb amputees was developed and tested. It is equipped with sensing devices and actuators allowing hand movements; myoelectric signals are detected by Myo armband with 8 ElectroMyoGraphic (EMG) electrodes, a 9-axis Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE) module. All data are received through HM-11 BLE transceiver by Arduino board which processes them and drives actuators. Raspberry Pi board controls a touchscreen display, providing user a feedback related to prosthesis functioning and sends EMG and IMU data, gathered via the armband, to cloud platform thus allowing orthopedic during rehabilitation period, to monitor users’ improvements in real time. A GUI software integrating a machine learning algorithm was implemented for recognizing flexion/extension/rest gestures of user fingers. The algorithm performances were tested on 9 male subjects (8 able-bodied and 1 subject affected by upper-limb amelia), demonstrating high accuracy and fast responses.

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Sarcopenia is a highly prevalent, age-related muscle disorder associated with adverse outcomes. It is very important from a medical point of view to periodically monitor patients at risk of developing sarcopenia in order to early detect its onset or progression through objective and specific indicators. Today, the emerging Internet of Things (IoT)-enabling technologies allow us to create innovative, wearable, and non-invasive systems that can offer useful clinical support in this area. This work is focused on the use of combined hardware and software technologies, enabling the IoT, in order to monitor people suffering from sarcopenia by offering a high value-added service in the field of the Ambient Assisted Living (AAL). In addition to the description of the proposed system architecture, a validation of the entire system is also included, from both a performance and a functional point of view. Test beds have been carried out by using the independent replications method, and all measurements related to the identified sarcopenia parameters are characterized by a 95% confidence interval with a 5% maximum relative error. The implementation of these technologies as a supporting clinical tool used in a specific setting could significantly impact the life and independence of the sarcopenic frail elderly population.