Antonino Proto

Nanogenerators for Human Body Energy Harvesting

Humans generate remarkable quantities of energy while performing daily activities, but this energy usually dissipates into the environment. Here, we address recent progress in the development of nanogenerators (NGs): devices that are able to harvest such body-produced biomechanical and thermal energies by exploiting piezoelectric, triboelectric, and thermoelectric physical effects. In designing NGs, the end-users comfort is a primary concern. Therefore, we focus on recently developed materials giving flexibility and stretchability to NGs. In addition, we summarize common fabrics for NG design. Finally, the mid-2020s market forecasts for these promising technologies highlight the potential for the commercialization of NGs because they may help contribute to the route of innovation for developing self-powered systems.

Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting

In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities—walking, walking up and down stairs, jogging and running—were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW–74 µW using both transducers for each joint.

The Effect of Window Length on the Classification of Dynamic Activities through a Single Accelerometer

This paper investigates how different window sizes for feature extraction and classification affect the accuracy of daily living locomotors activity recognition through accelerometers. A comprehensive data set was collected from 9 healthy subjects performing walk, stair descending and stair ascending while carrying an accelerometer on the waist. Nearest neighbor based classification has been used because of its simplicity and flexibility. The findings show that, by increasing window length, the system accuracy increases, but it produces delays in real time detection/alert of the activity. From the experiments it is concluded that a 2 seconds (2 s) time window may represent a trade-off for the detection of these mentioned activities in a real-time scenario, as it produces 91.7 percent of accuracy.

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the mid-swing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities.

Spatio-temporal gait parameters as estimated from wearable sensors placed at different waist levels

Wearable devices are able to capture movement-related characteristics from inertial sensors integrated in them. Many spatio-temporal parameters of gait can be estimated by the acquisition of inertial data, but their accuracy depends on the placement of the devices on the body, as well as on the numerical values chosen for the estimation techniques. In this work, three inertial sensors placed at three different heights of the trunk are used to collect data from healthy adult participants walking at three different speeds. Step length and step velocity were calculated from accelerometer data. For the estimation of these parameters high-pass filtering is required: fifteen different values of the filter cut-off frequency were analyzed for the subsequent step length estimation. The results were compared against those measured from a marker-based movement analysis system. Estimation accuracy of both step length and step velocity resulted significantly affected by both sensor location and cut-off frequency of the filter. These preliminary results suggest that placing the sensor too low leads to an increased estimation error, while frequencies up to around 1 Hz lead to acceptable results, with a significant decrease in estimation accuracy above that value.

A new microcontroller-based system to optimize the digital conversion of signals originating from load cells built-in into pedals

During cycling, the measurement of forces exerted on the pedal is used to monitor the level of training and to maximize the efficiency of pedaling. In rehabilitation, the force measurement can be used to monitor the functional recovery of a patient during a therapy. In these situations, it is useful to quantify with high resolution these variables. In this work a solution to remove the DC offset at the input of an AD converter for force measurement systems, based on strain gauges load cells, is presented. This circuit has been integrated into a device used in sports and in rehabilitation contexts, that relies on a couple of cycling instrumented pedals. The system designed in this work aims at obtaining these results in a simple way and with its complete integration into the control circuit of the instrumented pedals.

Classification of Myopotentials of Hand’s Motions to Control Applications

Realization of the system for classification of hand’s gestures is described in this paper. The first goal was to create hardware that would be able to measure signal of myopotentials for computer analysis without external noise and with right amplification. The second goal was to program an algorithm which could classify specific gestures of hand. Hardware prototype of four measuring channels was created by combination of 2nd order filters and right amount amplification. The user is isolated from the power source using galvanic isolation because of usage of active electrodes. For digitizing the data, the Arduino Nano microcontroller was selected and programmed using defined communication protocol. The computer software is programmed in C# programming language. Signal processing and drawing to user interface is in real time. The one of five possible gestures that user made is chosen using fuzzy logic and designed system of scaling.

Investigation of a Possibility of ECG and PPG Common Measurement

Electrocardiography and Photoplethysmography are basic investigative methods used in healthcare. As the ECG and the PPG are non-invasive methods. The principle consists in ECG recording of electrical activity of the heart and the result of this sensing is a graph plotting—electrocardiogram. PPG is one of the main methods of measurement plethysmography when the result is a graphic record of pulse wave. The main function of PPG is the volumetric flow measurement of blood (e.g. in atherosclerosis, which is possible by vascular permeability derive their rigidity). Both of these methods are used for monitoring of cardiovascular system. It would be advantageous to have possibility to measure ECG and PPG from one place on human body. It would be also advantageous to have only one device and reduce number or size of sensors or electrodes. In this paper we describe developing such system and also investigation the ideal place for placing of sensors for satisfactory measurement.

Thermal Energy Harvesting on the Bodily Surfaces of Arms and Legs through a Wearable Thermo-Electric Generator

This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 μW. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities.