The waste in the perishable goods supply-chain has prompted many global organizations (e.g., FAO and WHO), to develop the Hazard Analysis and Critical Control Points (HACCP) protocol that ensures a high degree of food quality, minimizing the losses in all the stages of the farm-to-fork chain. It has been proven that good warehouse management practices improve the average life of perishable goods. The advances in wireless sensors network (WSN) technology offers the possibility of a “smart” storage organization. In this paper, a low cost reprogrammable WSN-based architecture for functional warehouse management is proposed. The management is based on the continuous monitoring of environmental parameters (i.e., temperature, light exposure and relative humidity), and on their combination to extract a spatial real-time prediction of the product shelf life. For each product, the quality decay is computed by using a 1st order kinetic Arrhenius model to the whole storage site area. It strives to identify, in a way compatible with the other products’ shelf lives, the position within the warehouse that maximizes the food expiration date. The shelf life computing and the “first-expired first-out” logistic problem are entrusted to a Raspberry Pi-based central unit, which manages a set of automated pallet transporters for the displacement of products, according to the computed shelf lives. The management unit supports several commercial light/temperature/humidity sensor solutions, implementing ZigBee, Bluetooth and HTTP-request interfaces. A proof of concept of the presented pro-active WSN-based architecture is also shown. Comparing the proposed monitoring system for the storage of e.g., agricultural products, with a typical one, the experimental results show an improvement of the expected expiration date of about 1.2 ± 0.5 days, for each pallet, when placed in a non-refrigerated environment. In order to stress the versatility of the WSN solution, a section is dedicated to the implemented system user interfaces that highlight detecting critical situations and allow timely automatic or human interventions, minimizing the latter.

Abnormal gait and postural instability are common disorders in people affected by Parkinson's disease (PD). This paper proposes an embedded cyber-physical system for the identification and the real-time extraction of highly selective diagnostic indexes for PD patients. A noninvasive wearable and wireless architecture for both gait analysis and postural instability detection has been proposed and implemented on a programmable hardware. The combined analysis of electroencephalography and electromyography allows studying the motor cortex activity through the movement-related potentials, determining a novel set of indexes that could be used for the PD diagnosis and classification. In a future perspective of an application-specific integrated circuit implementation, the real-time data processing has been fully realized on the Altera Cyclone V field-programmable gate array (FPGA), without interactions with embedded processor architecture. Referring to an Altera Cyclone V SE 5CSEMA5F31C6N device, the whole implemented architecture exploits 90% of the available FPGA adaptive logic modules, 74% of the manageable registers, and 10.3% of the total memory, as well as 29.7% wires utilization. Furthermore, the system is able to provide the outputs in about 57 ms with a dynamically power dissipation of 89 mW. The platform has been tested in vivo on two Parkinson's patients and two healthy subjects (control group) covering three typical diagnostic scenarios: PD versus controls, drug treatment evaluation, and involuntary movements detection.

The authors present wearable noninvasive electronics that prevent a human from falling. It deducts a probable fall from EEG and EMG information and provides a real-time alarm signal for protection.

The growing demand for the solid-state meters for power and energy measurement leads to the fully integrated Hall-sensor-based microsystem solutions. In this paper, a silicon-on-insulator (SOI) Hall-sensor-based microsystem for energy measurements with dynamic offset cancellation was described. The Hall sensor is used to multiply the line voltage and the line current, giving an output voltage proportional to the instantaneous power. Furthermore, the voltage at the Hall output is proportional to the line active power and can be further processed. By converting the sensor output voltage to a digital signal using a delta-sigma (DeltaSigma) modulator followed by a digital filtering, the energy consumption is observed at the end of the processing chain. The entire microsystem has been integrated in an experimental 0.5-mum fully depleted SOI process and has a measured output error of less than plusmn1.5%