This thesis projects presents a system for gathering and using data in the context of a smarthome research enviroment. The system was developed at the Human Health and ActivityLaborty, H2Al, at Luleå University of Technology and consists of two distinct parts. First, a data export application that runs in the H2Al enviroment. This application syn-chronizes data from various sensor systems and forwards the data for further analysis. Thisanalysis was performed in the iMotions platform in order to visualize, record and export data.As a delimitation, the only sensor used was the WideFind positional system installed at theH2Al. Secondly, an activity recognition application that uses data generated from the iMotionsplatform and data export application. This includes several scripts which transforms rawdata into labeled datasets and translates them into activity recognition models with the helpof machine learning algorithms. As a delimitation, activity recognition was limited to falldetection. These fall detection models were then hosted on a basic server to test accuracyand to act as an example use case for the rest of the project. The project resulted in an effective data gathering system and was generally successful asa tool to create datasets. The iMotions platform was especially successful in both visualizingand recording data together with the data export application. The example fall detectionmodels trained showed theoretical promise, but failed to deliver good results in practice,partly due to the limitations of the positional sensor system used. Some of the conclusions drawn at the end of the project were that the data collectionprocess needed more structure, planning and input from professionals, that a better positionalsensor system may be required for better fall detection results but also that this kind of systemshows promise in the context of smart homes, especially within areas like elderly healthcare.

Among the elderly, falling represents a major threat to the individual health, and is considered as a major source of morbidity and mortality. In Sweden alone, three elderly are lost each day in accidents related to falling. The elderly who survive the fall are likely to be suffering from decreased quality of life. As the percentage of elderly increase in the population worldwide, the need for preventive methods and tools will grow drastically in order to deal with the increasing health-care costs. This report is the result of a conceptual study where an algorithm for individualized motion monitoring and pre-impact fall detection is developed. The algorithm learns the normal state of the wearer in order to detect anomalous events such as a fall. Furthermore, this report presents the requirements and issues related to the implementation of such a system. The result of the study is presented as a comparison between the individualized system and a more generalized fall detection system. The conclusion is that the presented type of algorithm is capable of learning the user behaviour and is able to detect a fall before the user impacts the ground, with a mean lead time of 301ms. / Bland äldre är risken för att drabbas av fallrelaterade skador överhängande, ofta med svåra fysiska skador och psykiska effekter som följd. Med en ökande andel äldre i befolkningsmängden beräknas även samhällets kostnad för vård att stiga. Genom aktiva samt preventiva åtgärder kan graden av personligt lidande och fallre- laterade samhällskostnader reduceras. Denna rapport är resultatet av en konceptuell studie där en algoritm för aktiv, individanpassad falldetektion utvecklats. Algoritmen lär sig användarens normala rörelsemönster och skall därefter särskilja dessa från onormala rörelsemönster. Rapporten beskriver de krav och frågeställningar som är relevanta för utvecklingen av ett sådant system. Vidare presenteras resultatet av studien i form av en jämförelse mellan ett individanpassat och generellt system. Resultatet av studien visar att algoritmen kan lära sig användarens vanliga rörelsemönster och därefer särskilja dessa från ett fall, i medelvärde 301ms innan användaren träffar marken.

The following paper describes a fall detection and activity monitoring system with position detection based on Zigbee transceivers.The main objective is to reduce the time taken for emergency personnel to respond to falls among the elderly. Especially when the victim is unconscious or delirious, position tracking reduces location determination time within a busy hospital or nursing home environment and facilitates immediate treatment. Reduced response times correlate to decreased morbidity and mortality rates. Background is provided on the major wireless network advances currently deployed in a healthcare setting for asset and personnel tracking, etiology of falls, and several methods of detecting falls using sensors and image processing techniques. Data analysis proves that a precise coordinate tracking system was infeasible using the XBee RF module (based on the Zigbee protocol) due to environmental noise, a poor antenna construction and lack of precise signal strength measurements. A primitive scheme with lower resolution and higher reliability associating a single location with each Zigbee transceiver was employed. A pedometer function was added to the project to monitor the user’s daily activity and to potentially serve as a predictor of falls through the interpretation of mobility and gait patterns related to step counts.

No description available.

Context. Generally, health monitoring systems are used in hospitals, which are pricey and gigantic. But with the up gradation of sensors and modules, these devices are also available in portable sizes. These devices are divided into different types according to the disease. So, our project aims to provide a device with multiple parameters monitoring with fall detection, and it is budget-friendly.  Objectives. The objective of our project is to provide freedom to users and monitor simultaneously, using IoT and sensors. People with an illness that may be physical or mental, children, and older people with some issues. They are the users of our idea. Generally, they need to be monitored by a person, which is costly and requires endurance. So, the main objective is to monitor the patient’s health condition and alert in an emergency and store the data on the user’s health.  Methods. After a lot of observation, we found that we can monitor the patient’s health status using an ESP32 Wroom dev kit, which is a microcontroller that consists of Bluetooth and Wi-Fi. We use an MPU6050 accelerometer that detects the falling and motion of the user using three axial movements. We use MAX30102 pulse oximetry which measures the oxygen level, pulse rate, and temperature. Along with these, we use a mobile application that receives data and stores it.  Results. The device reads the parameters regularly and stores the data in the cloud or mobile application. It contains a push button that alerts the relatives and respected authorities. It transmits the location. Finally, it will trigger the command of alerting when the user falls. Conclusions. Every person can use our health monitoring system. The person should wear the device properly and be connected to the Wi-Fi. Once the fall is detected, the contacts are notified. And the detection is more accurate. Regular usage of the device increases the accuracy and analysis of the user’s health.

Wearable wireless sensor devices, which are implemented by deploying sensor nodes on objects, are widely utilized in a broad field of applica-tions, especially in the healthcare system for improving the quality of life or monitoring different types of physical data from the observed objects. The aim of this study is to design an in-home, small-size and long-term wearable fall detection system in wireless network by using barometric pressure sensing for elderly or patient who needs healthcare monitoring. This threshold-based fall detection system is to measure the altitude of different positions on the human body, and detect the fall event from that altitude information. As a surveillance system, it would trigger an alert when the fall event occurs so that to protect people from the potential life risk by immediate rescue and treatment. After all the performances evaluation, the measurement result shows that standing, sitting and fall state was detected with 100% accuracy and lying on bed state was detected with 93.3% accuracy by using this wireless fall detection system. Furthermore, this system with low power consumption on battery-node can operate continuously up to 150 days.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.

Abstract About one third of home-dwelling older people suffer a fall ech year. The most consuming falls occur when the person is alone and unable to get up, resulting in long lies which are associated with institutionalisation and high morbidity-mortality rate. Even though personal emergency response systems provide applications to call for help, older people are not always able or willing to activate them. Hence, an automatic fall detection system is an important setting. Even though pilot applications and commercial fall detection systems exist, the real-life validation of these systems is scant. The aim of this study was to develop a validated acceleration-based method for fall detection to be adapted for real-life applications among older people. Methods capable of discriminating between falls and activities of daily living (ADL) were determined based on laboratory tests. The threshold-based algorithms were validated with intentional falls in 20 middle-aged test persons and ADL in 20 middle-aged and 21 older people. The algorithm for the waist with impact and end posture detection was able to discriminate falls from ADL with 97% sensitivity and 100% specificity. In order to validate the fall detection system, a field test was performed with 16 residents in elderly care units wearing a wireless sensor. During the 6-month test period, acceleration data from five real-life falls were collected. One of the falls resulted in a hip fracture. These falls showed similar features as intentional falls. However, high pre impact velocity was detected in the case with a fracture, but not in all falls with preventative actions. The system had a fall detection sensitivity of 71.4% with a false alarm rate of 1.1 alarms over a 24-hour time period in this real-life pilot test. The data from real-life falls provide important material for further development of fall detection and studies on fall mechanism and fall prevention. / Tiivistelmä Kotona asuvista yli 65-vuotiaista kaatuu vuosittain kolmannes. Mikäli kaatunut ei kykene nousemaan omin neuvoin, avun saaminen saattaa viivästyä. Tämä suurentaa sekä laitoshoitoon joutumisen todennäköisyyttä että kuoleman riskiä. Erilaisia hälytysjärjestelmiä on kyllä saatavilla, mutta ikääntyneet eivät aina kykene käyttämään niitä tai eivät jostain syystä halua tehdä hälytystä. Tämän vuoksi automaattiselle kaatumishälyttimelle on tarvetta. Tässä tutkimuksessa kehitettiin ja testattiin ikääntyneiden tarpeisiin soveltuva kiihtyvyysanturiin perustuva kaatumisen tunnistumenetelmä. Aineisto koottiin laboratorio-olosuhteissa kokeilla, joihin osallistui sekä nuoria että keski-ikäisiä. Raja-arvoon perustuvia tunnistusalgoritmeja testattiin 20 keski-ikäisen ohjeistettujen testikaatumisten sekä 20 keski-ikäisen ja 21 ikääntyneen arkisten askareiden tuottamalla datalla. Kaatumistapahtuman impaktin ja loppuasennon tunnistaminen vyötäröltä mitatuista kiihtyvyysarvoista erotteli kaatumisen muusta liikkeestä 95 % sensitiivisyydellä ja 100 % spesifisyydellä. Tunnistusmenetelmää testattiin kenttäkokeessa, jossa 16 ikääntynyttä hoitokodin asukasta piti vyötäröllään mittauslaitetta. Kuuden kuukauden aikana kiihtyvyyssignaali saatiin viidestä kaatumisesta. Yksi niistä aiheutti lonkkamurtuman. Analyysin mukaan näiden todellisten kaatumisten kiihtyvyyssignaalit muistuttivat testikaatumisia. Lonkkamurtumatapauksessa ennen impaktia mitattu nopeus oli erittäin korkea. Vastaavaa ei havaittu tapauksissa, joissa oli merkkejä siitä, että kaatumista oli yritetty estää. Kenttäkokeessa kaatumishälytysjärjestelmän sensitiivisyys oli 71.4 % ja vääriä hälytyksiä oli 1.1 vuorokaudessa. Tutkimuksessa saatua tietoa tosielämän kaatumistapahtumista voidaan käyttää hyväksi kehitettäessä kaatumisten ehkäisyä, niiden mekanismin tutkimista sekä kaatumisen tunnistusta kiihtyvyysanturien avulla.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.