2 State of the Art¶

2.1 Apps in Healthcare¶

As of 2022, there were around 6.4 billion of smartphone mobile network subscriptions (https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/), which is equivalent to around 79% of the world's population. Besides that, the average number of hours spent on mobile phones ranges from 3-4 hours a day (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685243/) up to even 5-7 hours (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9368281/). These habits are becoming detrimental to the well-being of people all around the globe, as it is shown in various studies (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9368281/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491487/). Since this problem is here to stay, at least from now, we can use this problem to our advantage, namely in health care.

In the past several years, health care professionals have begun using mobile devices, transforming many aspects of clinical practice and allowing the rapid emerge of medical software applications (apps).(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029126/) They help professionals with many important tasks, such as: patient management and monitoring; information and time management; communications and consulting; reference and information gathering; clinical decision-making; medical education and training; and even time management. [[[1. Wallace S, Clark M, White J. ‘It’s on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study. BMJ Open. 2012 Aug;2:e001099.
4. Ozdalga E, Ozdalga A, Ahuja N. The smartphone in medicine: a review of current and potential use among physicians and students. J Med Internet Res. 2012;14(5):e128.
7. Mosa AS, Yoo I, Sheets L. A systematic review of health care apps for smartphones. BMC Med Inform Dec Mak. 2012 Jul;12:67.
8. Divali P, Camosso-Stefinovic J, Baker R. Use of personal digital assistants in clinical decision making by health care professionals: a systematic review. Health Informatics J. 2013;19(1):16–28.]]] There are already many apps in this field, making the emerge of many legislations (https://www.sciencedirect.com/science/article/pii/S1386505623001594)and definitions (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664324/) around these topics.

With that in mind, it is important to develop new apps for many devices and clinical problems, such as the app we developed for step counting and health monitoring, using the Vital Jacket (https://ieeexplore.ieee.org/document/5482268)

2.2 Vital Jacket¶

The Vital Jacket® is a groundbreaking wearable vital signs monitoring system that seamlessly integrates textiles with microelectronics. Initially developed at the University of Aveiro, Portugal, by the IEETA institute, the device has undergone significant evolution, transitioning from a jacket to a more compact T-shirt format. The system has been licensed to a start-up company called Biodevices, S.A., which further refined the prototypes, focusing on applications in cardiology and high-performance sports.

The device, certified to meet ISO9001 and ISO13485 standards, has received approval as a Medical Device for the European market, complying with the MDD directive 42/93/CE and holding the CE1011 mark. The Vital Jacket® is designed for use in diverse clinical scenarios, such as hospitals, homes, or on the move, providing continuous and high-quality vital signs monitoring and incorporates various sensors for monitoring parameters like ECG, temperature, respiration, movement, posture, actigraphy, oxygen saturation, and more.

The presentation of the Vital Jacket® highlights its real-world applications, with live demonstrations during congress days. The device has proven its efficacy in cardiology, offering features like real-time ECG monitoring and heart rate data transmission to personal devices. Overall, the Vital Jacket® represents a pioneering advancement in wearable health technology, providing a practical and comfortable solution for continuous vital signs monitoring in various healthcare and sports settings.

https://www.biodevices.pt/en/sdk-2/
https://ieeexplore.ieee.org/document/5482268
https://www.biodevices.pt/en/vitaljacket-holter-2/

2.3 3-axis accelerometer and step counting¶

An accelerometer is a sensor that measures acceleration, which is the rate of change of velocity of an object. Acceleration can be caused by various factors, including movement, gravity, or vibration. Accelerometers are widely used in various devices, such as smartphones, fitness trackers, inertial navigation systems, and industrial equipment, to detect and measure acceleration. There are different types of accelerometers, but one of the most common types is the Micro-Electro-Mechanical Systems (MEMS) accelerometer, which is widely used in consumer electronics due to its small size and cost-effectiveness. Here's a basic explanation of how a MEMS accelerometer works:

MEMS accelerometers work based on the principle of inertia. According to Newton's second law of motion, the force acting on an object is equal to the mass of the object multiplied by its acceleration (F = m * a). In an accelerometer, the mass is usually a small, suspended proof mass. The proof mass is typically suspended by tiny springs or flexures that allow it to move in response to acceleration. The suspension system is designed to be highly sensitive to acceleration forces while minimizing any damping effects. In order to measure the movement of the proof max, capacitive sensing is used. The proof mass is located between two, or more, fixed capacitor plates and the capacitance changes as the distance between the plates and the proof mass changes. This change in capacitance is detected by the electronics within the accelerometer and is then converted to an electric signal. This signal is processed and filtered and the resulting output is the acceleration experienced along the measured axis.

Algorithms analyze the processed data to identify repetitive patterns that correspond to steps. Walking and running generate characteristic acceleration patterns, and the algorithm aims to recognize these patterns. Steps are often associated with peaks in the acceleration data. A threshold is set, and when the acceleration surpasses this threshold, it may indicate the occurrence of a step. The rising and falling edges of the acceleration peaks are used to define the start and end of each step. The algorithm keeps track of the number of times the acceleration data crosses the threshold, essentially counting the steps. More sophisticated algorithms may incorporate machine learning techniques to adapt to variations in individual walking styles and filter out false positives.

https://www.vertexknowledge.com/post/how-does-a-smart-watch-count-steps-tech-knowledge
https://arxiv.org/ftp/arxiv/papers/1801/1801.02336.pdf

2.4 Step Counter as a health indicator¶

In the world we live today, a sedentary lifestyle is very common, leading to 5 million deaths a year that could be avoided if a more active lifestyle was adopted and prioritized. Besides promoting physical well-being, physical activity also contributes to mental health. (https://www.who.int/news/item/25-11-2020-every-move-counts-towards-better-health-says-who). As Dr. Ruediger Krech, the Director of Health Promotion of the World Health Organization stated, “Physical activity of any type, and any duration can improve health and wellbeing” (https://www.who.int/news/item/25-11-2020-every-move-counts-towards-better-health-says-who), meaning that walking, that is free and requires no special training, is an easy way of promoting a healthier lifestyle (https://www.sciencedirect.com/science/article/pii/S2095254621001010).

The Centers for Disease Control and Prevention (CDC) recommends taking 10 000 steps daily, which is approximately equivalent to 8 kilometers. Lower than half of this value is a sign of a sedentary lifestyle (https://www.medicalnewstoday.com/articles/how-many-steps-should-you-take-a-day#by-age).

Step counter devices have been widely used since they encourage the individuals to meet their daily step goals by turning physical activity into a challenge, motivating them to choose the stairs instead of the elevator, for example (https://www.medicalnewstoday.com/articles/how-many-steps-should-you-take-a-day#by-age).

Nowadays, with the development of technology, most smartphones and wearables allow controlling the number of steps taken as a health indicator (https://www.sciencedirect.com/science/article/pii/S2095254621001010). Step counting devices are based on accelerometry motion sensing. These record a step when the acceleration surpasses a defined threshold value, allowing an immediate response. Examples of step counters are Fitbit devices, which are wearable inertial measurement units (IMUs) that besides recording steps, also acquire other data such as plethysmography to measure heart rate. These data are stored in smartphone apps, and can be viewed by the user (https://www.nature.com/articles/s41746-022-00696-5). There are already various applications that help with step counting that will be approached in the next section.

Several studies regarding step counters have been performed that proved that a higher step count is associated with reduced risk of health problems, such as cardiovascular diseases https://www.sciencedirect.com/science/article/pii/S2095254621001010, chronic obstructive pulmonary disease (https://pubmed.ncbi.nlm.nih.gov/36775005/) and overall mortality (https://www.ajconline.org/article/S0002-9149(22)00729-9/fulltext). Step counters are also used in rehabilitation, helping individuals to recover from injuries or surgeries by gradually increasing their activity levels (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246942/).

Although the use of step counting devices brings several benefits, they have some limitations concerning the steps count or the traveled distance. It is also essential that these devices are used together with other forms of exercise besides walking, and healthcare professionals, so that the individuals are able to adjust goals according to their health needs (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488109/).

https://www.sciencedirect.com/science/article/pii/S2095254621001010
https://pubmed.ncbi.nlm.nih.gov/10993418/
https://pubmed.ncbi.nlm.nih.gov/36775005/
https://www.nature.com/articles/s41746-022-00696-5
https://www.ajconline.org/article/S0002-9149(22)00729-9/fulltext
https://www.who.int/news/item/25-11-2020-every-move-counts-towards-better-health-says-who
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488109/

2.5 Similar Apps¶

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488109/
https://www.mdpi.com/2227-7080/9/3/55
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010703/
https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-020-01020-8
https://www.goodhousekeeping.com/health-products/g28778836/best-step-counter-pedometer-apps/