Human Activity Recognition (HAR) is a growing field of research concerned with classifying human activities from sensor data. Modern smartphones contain numerous sensors that could be used to identify the physical activities of the smartphone wearer, which could have applications in sectors such as healthcare, eldercare, and fitness. This project aims to use smartphone sensor data together with machine learning to perform HAR on the following human locomotion activities: standing, walking, running, ascending stairs, descending stairs, and biking. The classification was done using a random forest classifier. Furthermore, in the special case of walking, an algorithm that can count the number of steps in a given data sequence was developed. The step counting algorithm was not based on a previous implementation and could therefore be considered novel. The step counter achieved a testing accuracy of 99.1\% and the HAR classifier a testing accuracy of 100\%. It is speculated that the abnormally high accuracies can be attributed primarily to the lack of data diversity, as in both cases only two persons collected the data. / Mänsklig aktivitetsigenkänning är ett växande forskningsområde som handlar om att klassificera mänskliga aktiviteter från sensordata. Moderna mobiltelefoner innehåller många sensorer som kan användas för att identifiera de fysiska aktiviteterna som bäraren utför, vilket har tillämpningar inom sektorer som sjukvård, äldreomsorg och personlig hälsa. Detta projekt använder sensordata från mobiltelefoner tillsammans med maskininlärning för att utföra aktivitetsigenkänning på följande aktiviteter: stå, gå, springa, gå uppför trappor, gå nedför trappor och cykla. Klassificeringen gjordes med hjälp av en ``random forest''-klassificerare. Vidare utvecklades en algoritm som kan räkna antalet steg i en given datasekvens som samlats in när användaren går. Stegräkningsalgoritmen baserades inte på en tidigare implementering och kan därför betraktas som ny. Stegräknaren uppnådde en testnoggrannhet på 99,1\% och aktivitetsigenkänningen en testnoggrannhet på 100\%. De oväntat höga noggrannheterna antas främst bero på bristen av diversitet i datan, eftersom den endast samlades in av två personer i båda fallen. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Context. Reminder system offers flexibility in daily life activities and assists to be independent. The reminder system not only helps reminding daily life activities, but also serves to a great extent for the people who deal with health care issues. For example, a health supervisor who monitors people with different health related problems like people with disabilities or mild dementia. Traditional reminders which are based on a set of defined activities are not enough to address the necessity in a wider context. To make the reminder more flexible, the user’s current activities or contexts are needed to be considered. To recognize user’s current activity, different types of sensors can be used. These sensors are available in Smartphone which can assist in building a more contextual reminder system. Objectives. To make a reminder context based, it is important to identify the context and also user’s activities are needed to be recognized in a particular moment. Keeping this notion in mind, this research aims to understand the relevant context and activities, identify an effective way to recognize user’s three different activities (drinking, walking and jogging) using Smartphone sensors (accelerometer and gyroscope) and propose a model to use the properties of the identification of the activity recognition. Methods. This research combined a survey and interview with an exploratory Smartphone sensor experiment to recognize user’s activity. An online survey was conducted with 29 participants and interviews were held in cooperation with the Karlskrona Municipality. Four elderly people participated in the interview. For the experiment, three different user activity data were collected using Smartphone sensors and analyzed to identify the pattern for different activities. Moreover, a model is proposed to exploit the properties of the activity pattern. The performance of the proposed model was evaluated using machine learning tool, WEKA. Results. Survey and interviews helped to understand the important activities of daily living which can be considered to design the reminder system, how and when it should be used. For instance, most of the participants in the survey are used to using some sort of reminder system, most of them use a Smartphone, and one of the most important tasks they forget is to take their medicine. These findings helped in experiment. However, from the experiment, different patterns have been observed for three different activities. For walking and jogging, the pattern is discrete. On the other hand, for drinking activity, the pattern is complex and sometimes can overlap with other activities or can get noisy. Conclusions. Survey, interviews and the background study provided a set of evidences fostering reminder system based on users’ activity is essential in daily life. A large number of Smartphone users promoted this research to select a Smartphone based on sensors to identify users’ activity which aims to develop an activity based reminder system. The study was to identify the data pattern by applying some simple mathematical calculations in recorded Smartphone sensors (accelerometer and gyroscope) data. The approach evaluated with 99% accuracy in the experimental data. However, the study concluded by proposing a model to use the properties of the identification of the activities and developing a prototype of a reminder system. This study performed preliminary tests on the model, but there is a need for further empirical validation and verification of the model. / +46707560843