Better Physical Activity Classification using Smartphone Acceleration Sensor

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities

Synthetic Sensor Data for Human Activity Recognition

Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance. In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier

Physical Activity Classification for Elderly People in Free-Living Conditions

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free-living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). A support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single sensor or multisensor) to correctly classify the ADLs of older people in free-living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. By contrast, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.<br/

Anturidatan analyysi unen vaikutuksista ja vaikutuksista uneen

Tiivistelmä. Tässä kandidaatintyössä perehdytään lifelog-antureihin, erityisesti puettaviin laitteisiin ja niillä mitattaviin suureisiin. Lisäksi analysoidaan anturidataa unen vaikutuksista mielialaan, aktiivisuuteen sekä seuraan ja näiden vaikutuksista uneen. Puettavat laitteet mahdollistavat ihmisten elintapojen seurannan, mikä auttaa ihmisiä parantamaan elintapojaan. Älykellot, -rannekkeet ja muut puettavat laitteet ovat kasvattaneet suosiotaan ja parantunut tekniikka mahdollistaa yhä tarkemmat mittaukset. Tutkimuskäytössä puettava laite on helppo tapa seurata koehenkilöä hänen normaalissa elämässään. Laajempi ja parempi seuranta auttaa myös sairauksien tutkimisessa ja havaitsemisessa. Työn alussa kirjallisuuskatsauksessa perehdytään lifelog-antureihin ja niiden käyttöön tutkimuksessa. Käydään läpi, millä antureilla mitataan eri suureita ja miten anturit toimivat. Toisessa osassa analysoidaan laajaa lifelog-antureilla kerättyä tietoaineistoa. Tutkitaan erityisesti unen vaikutusta seuraavan päivän mielialaan, aktiivisuuteen ja seuraan. Löydettiin positiivista korrelaatiota mielialan positiivisuuden ja unen välillä. Negatiivista korrelaatiota löydettiin yksin ja vähintään kahden ihmisen seurassa vietetyn ajan ja unen välillä. Tutkittiin myös mielialan, aktiivisuuden ja seuran vaikutusta seuraavan yön uneen. Positiivista korrelaatiota unen kanssa löytyi mielialan positiivisuuden ja aktiivisuuden välillä sekä negatiivista korrelaatiota unen ja yksin vietetyn ajan välillä.Analysis of sensor data on the effects of sleep and effects on sleep. Abstract. This bachelor’s thesis focuses on lifelog sensors, especially on wearable devices and qualities measured on them. Additionally, sensor data is analyzed on the effects of sleep on mood, activity and company, and their effects on sleep. Wearable devices allow people to follow their lifestyles which helps people improve their lifestyles. Smartwatches, wristbands, and other wearable devices have grown in popularity and improved technology allows for increasingly accurate measurements. In research use, the wearable device is an easy way to track a subject in his or her normal life. Wider and better tracking will also help in the investigation and detection of different diseases. At the beginning of the thesis, the literature review introduces lifelog sensors and their use in research. It is explained, which sensors are used to measure different quantities and how the sensors work. The second part focuses on analyzing a large dataset collected using lifelog sensors. In particular, the effect of sleep on the mood, activity, and company during the next day is studied. Positive correlation was found between positivity of the mood and sleep. Negative correlations were found between time spent alone and with more than two people, and sleep. The effect of mood, activity, and company on the next night’s sleep was also studied. Positive correlations between sleep, and positivity of the mood and activity were found. A negative correlation was found between sleep and time spent alone

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms

Physical Activity Classification for Elderly People in Free Living Conditions

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). Support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single-sensor or multi-sensor) to correctly classify the ADLs of older people in free living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. On the other hand, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications

Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method