Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique.

In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.

Punctual mechanical oscillation in modulation of muscular tonus in children with spasticity

Spasticity is a motor condition present in 75 to 88% of children with Cerebral Palsy (CP). One form of treatment is called punctual mechanical oscillation (PO). The current study aimed to study different protocols for the application of PO and the magnitude of their effects. In total, 7children with medical diagnosis of CP and ICD (International Classification of Diseases) were included. The first intervention protocol (Int1) consisted of the application of PO to the spastic muscle tendon and the second intervention protocol (Int2) to the muscle belly ofthe spastic antagonist muscle. For evaluation, the Modified Ashworth Scale (MAS) was used, while simultaneously capturing the mechanomyography (MMG) signals. Data were collected pre-intervention and 1 (Post1), 15 (Post15), 30 (Post30), 45 (Post45), and60 (Post60) minutes after the interventions. The MAS values (median ± interquartile range) post intervention were statistically lower when compared to the pre values in the 2 protocols studied; in Int1between Pre (2 ± 0) andPost15 (0 ± 1.75), Post30 (0 ± 1), Post45 (1 ± 1),and Post60 (1 ± 1), and in Int2only between Pre (2 ± 1) and Post1 (0 ± 1).The values found in the MMG in both its temporal and spectral domains did not follow a pattern (p>0.05). The comparison between the protocols did not demonstrate statistical differences in any characteristics (MAS, MMGMF, and MMGRMS). However, PO was shown to be a therapeutic resource that modulated spasticity for up to 60 minutes after its application, and PO could contribute as a tool to aid the treatment of spasticity.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants.

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.