Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x-, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naïve Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition

Activity recognition in mental health monitoring using multi-channel data collection and neural network

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2020-2021Ecological momentary assessment (EMA) methods can be used to extract context related information by studying a subject’s behaviour in an environment in real-time. In mental health EMA can be used to assess patients with mental disorders by deriving contextual information from data and provide psychological interventions based on the behaviour of the person. With the advancements in technology smart devices such as mobile phone and smartwatch can be used to collect EMA data. Such a contextual information system is used in SyMptOMS, which uses accelerometer data from smartphone for activity recognition of the patient. Monitoring patients with mental disorders can be useful and psychological interventions can be provided in real time to control their behavior. In this research study, we aim to investigate the effect of multi-channel data on the accuracy of human activity recognition using neural network model by predicting activities based on data from smartphone and smartwatch accelerometer sensors. In addition to this the study investigates model performance for similar activities such as SITTING and LYING DOWN. Tri-axial accelerometer data is collected at the same time from smartphone and smartwatch using a data collection application. Features are extracted from the raw data and then used as input to a neural network. The model is trained for single data input from smartphone and smartwatch as well the data from sensor fusion. The performance of the model is evaluated by using test samples from collected data. Results show that model with multi-channel data achieves a higher accuracy of activity recognition than the model with only single-channel data source

Activity Recognition Using Accelerometers

Automaatsel tegevuse tuvastamisel on palju rakendusi, iseäranis tervise valdkonnas. Erinevate igapäevaeluliste tegevuste mõõtmine on kasulik, sest see võimaldab saada teavet terviseseisundite kohta, nagu näiteks ülekaalulisus, insult või kukkumine. Veelgi enam, erinevate kasutajasõbralike kantavate seadmete laialdane levik võimaldab koguda kolmeteljelise kiirendusanduri andmeid mittesegavalt ja diskreetselt. Käesolevas töös on tegevuse tuvastamiseks kasutatud kiirendusanduri andmed on pärit projektist SPHERE [1]. Kiirendusmõõtmised on tehtud nelja kantava seadmega, mis olid kinnitatud katseisiku randmetele ning jalgadele.Töö võrdleb otsustusmetsa (random forest) ning pika lühiajalise mäluga (LSTM) tehisnärvivõrkude võimet tuvastada 9 siseruumi tegevust: hambapesu, söömine, hambaniiditamine, riietumine/lahtiriietumine, (toidu) segamine, (toidu) pealemäärimine, kõndimine, käte pesemine, kirjutamine. Lisaks laiendatakse tegevuste hulka teadmata tegevusega. Suuremat tähelepanu pööratakse järgmistele teemadele: tunnuste eraldamine, kiirendusanduri aegrea tükeldamine, parameetrite ja hüperparameetrite häälestamine, mudeli treenimine, mudeli hindamine ning üldistusvõime. Tulemused näitavad, et kiirendusanduril põhinevaid ekstraheeritud tunnuseid kasutav otsustusmets ületab tuvastusvõimelt pisut kiirendusanduri mõõtetulemusi muutmata kujul kasutavat LSTM-võrku, ning seda nii 9 tegevuse tuvastamisel kui ka peale teadmata tegevuse lisamist.Activity recognition is considered to have a wide range of applications, especially in the health sector. The assessment of different activities of daily living is useful because it can highlight information related to a specific health condition such as obesity, overweight, stroke, or fall. Moreover, the prevalence of different user-friendly wearable devices enables collecting tri-axial accelerometer data in a non-intrusive and discrete manner.The accelerometer data used for activity recognition in this thesis is provided by SPHERE [1]. The accelerometer readings are recorded from four wearables attached on a single person's hands and legs.This thesis compares the capabilities for activity recognition of the random forest model and the long short-term memory neural network to discern among 9 in-door activities including brushing teeth, eating a meal, flossing, getting dressed/undressed, mixing (food), spreading (food), walking, washing hands, writing. In addition, the list of activities is extended with an unknown activity. Greater focus is given on the following topics: feature extraction, segmentation of the time-series accelerometer data, parameter and hyper-parameter tuning, model training, model evaluation and generalization capability. The results suggest that the random forest model using the accelerometer-based extracted features slightly outperforms the long short-term memory neural network using raw accelerometer data when the activity recognition task is limited on the 9 chosen activities, and, additionally, when the unknown activity is included

HASC2011corpus: Towards the Common Ground of Human Activity Recognition

UbiComp '11 Proceedings of the 13th international conference on Ubiquitous computing, September 17-21, 2011, Beijing, ChinaHuman activity recognition through the wearable sensor will enable a next-generation human-oriented biquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus

Human activity recognition for static and dynamic activity using convolutional neural network

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97%. The result indicated the ability of the model for human activity recognition purposes

Localizing Tortoise Nests by Neural Networks

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition