A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

Parkinson's disease (PD) is a chronic and degenerative disorder of body movement, affecting people between the ages of 50 and 60. Freezing of Gait (FOG) is a symptom of the advanced stage of PD, associated with gait disorders with prominent risk falls; these drops cause stress, pain and are the main cause of death by injury. In this research, was developed a hardware-based wireless system non-invasive for the acquisition of real-time data from PD patients presenting with FOG episodes to stimulate walking progression, prevent falls, and improve patients' lifestyles. This was achieved by placing a stimulator device near the posterior tibial nerve of the lower extremities. The patients' gait is automatically detected by a three-axis accelerometer coupled to an inertial measurement unit (IMU). To make the data is raised three typical scenarios of occurrence of FOG: walk in a straight line, turns of 180 degrees, up and down steps, the data will be read and stored in a processor and transmitted to the smartphone via Bluetooth. Detected the FOG, generates a signal that produces vibratory stimulation, helping to break the FOG or avoiding it. The features of the devices are: use of surface sensors, low cost, portable, easy use and lightweight, in addition, a smartphone is used for monitoring and processing. For the validation of the system was done analytically based on specificity, sensitivity and effectiveness.

The increase of temperature on earth's surface in recent years has significantly affected the health of humans, where the concept of heat stroke has become a disturbing situation, especially if we consider the increase in deaths caused by this condition. The effects caused by problems related to high temperatures have been of interest for studies where the technology can have an important role for the solution of the same ones. The present work shows the design of a system employing temperature and heartbeat sensors using IoT concept for detection heat stroke early in children, elderly people and adults of productive age exposed to high temperatures and high humidity in the southeast of Mexico. The results show that the system proposed is efficient and practically usable in life real.

Parkinson's disease (PD), the second most common progressive neurological disorder that affects 1% of the population over the 50 years. The clinical symptoms of PD are the most common resting tremor, bradykinesia, rigidity, postural instability, and the gait blocking (FOG), among others. The FOG is the episodic disability of a normal gait common in advanced PD, resulting in a reduction of the mobility by increasing the possibility and the risk of falls. This article will develop a wireless system based on free software and hardware for the acquisition of real-time data from patients with episodes of FOG. The main features of the acquisition device will be: Low cost, portable, low weight and will be based on superficial tri-axial sensors, as well as Android application is developed for monitoring. The acquired data will be read and processed using the discrete wavelet transform to determine the energy levels of wavelet to subsequently generate a vibrating alert signal to pacing mode to avoid an episode of fog with the aim of improving the style of life and prevent possible falls in patients with PD.

In this research, a system to objectively quantify the Finger Tapping Test (FTT) is presented. The main element of this system is the Finger-TApp application, developed in order to record the taps performed by the people evaluated for 10 seconds. The study was performed with 64 healthy participants, aged between 50 and 83 years, men and women. Initially they were analyzed in three age groups, but finding no greater differences between these groups, it was decided to analyze all of them in a single set. With the results of the amount of taps performed by each patient, the mean and standard deviation were calculated; based on these values, the Upper Standard Deviation (USD) and the Lower Standard Deviation (LSD) were calculated. Additionally, it was verified that the distribution of the amount of taps, in the range of ages of the evaluated participants, corresponds to a normal distribution. According to the results obtained, future work is proposed, including improvements in the system and evaluations of people with Parkinson's disease, in which case the results obtained in the current study will be taken as a normality reference.