Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system

Wearable baseado em internet das coisas para monitoramento de sonâmbulos

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de Bacharel no Curso de Ciência da Computação da Universidade do Extremo Sul Catarinense, UNESC.O sonambulismo é mais perigoso do que parece, vários incidentes foram relatados em que as pessoas praticam atividades perigosas durante os episódios. Esta pesquisa tem como objetivo aplicar o conceito de Internet das Coisas na integração de uma pulseira com um aplicativo móvel para gerenciar o deslocamento de sonâmbulos. A aplicação pode detectar o deslocamento de um sonâmbulo por meio de uma pulseira e alertar o evento em um dispositivo móvel mediante um servidor, a fim de que uma pessoa próxima possa auxiliá-lo. Para monitorar os movimentos do sonâmbulo e identificar seus deslocamentos foram usados sensores inerciais. Os resultados apontam que a aplicação tem bom desempenho em quartos com mais de 9m²

Compensatory movement simulation of subacromial impingement syndrome kinematics using an asymptomatic group for rehabilitative shoulder exercises

Rotator cuff tears are a common source of shoulder pain that requires conservative management or surgical intervention to heal and regain proper function. During both interventions, prescribed exercise programs are given to patients as they increase range of motion (ROM) and improve patient outcome scores. However, when tasked with completing exercises in the home, patient adherence usually decreases and is subjectively monitored by the patient themselves. Wearable sensor devices, such as smartwatches, demonstrate feasibility to objectively track shoulder exercise adherence using machine learning, but these algorithms require a broad range of training data in order to accurately classify exercise type. Further, to monitor shoulder exercise rehabilitation, sensor training data should include compensatory exercise performance associated with symptomatic individuals. However, capturing this movement data from a symptomatic population presents a number of challenges. To address this problem, the objective of this study was to determine if asymptomatic individuals can simulate compensatory movement cues associated with subacromial impingement during various rehabilitative shoulder exercises. Seventeen participants (10 asymptomatic and 7 symptomatic for subacromial impingement) performed twenty repetitions of six evidence-based shoulder exercises following standard and compensatory movement cues based on their group classification. Kinematics of the torso and upper limbs were collected to identify changes in maximum angle and ROM for torso, thoracohumeral and elbow joint angles. Time-series joint angle data were compared for the standard and compensatory conditions performed by the asymptomatic group using statistical parametric mapping (SPM). Symptomatic and asymptomatic (compensatory) were compared using maximum angle and ROM measures. Asymptomatic participants were successful in simulating compensatory movement cues based on changes in their time-series data. Differences occurred in the middle portion of the thoracohumeral elevation time-series profile during the flexion (p < 0.05), scaption (p < 0.05), and abduction (p < 0.05) exercises. Further, these simulated compensatory movements were similar to the movement patterns of some symptomatic participants. Overall, these results suggest that asymptomatic individuals can execute both standard and compensatory movement cues. The variability of the data collected represents a spectrum between worst-case compensatory and best-case proper movement for the six shoulder exercises performed. Further research is needed to better understand the range of symptomatic exercise performance in order to refine the movement cue instructions for asymptomatic individual performance. Data and findings from this work provide crucial groundwork towards the development of improved machine learning algorithms for sensor-based tracking of rehabilitative shoulder exercise program adherence and progression

Methods of human activity classification in buildings

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and detection of falling events of people. Two different approaches are proposed to integrate human activity recognition within smart homes. The first approach utilizes KNX standard-based devices to obtain room air quality data (humidity, CO2, temperature) and combine the obtained data with two wearable devices that provide movement-related data. The second approach simplifies, improves, and addresses a few of the shortcomings of the first approach, it utilizes different measuring devices with higher sampling rates. It examines multiple statistical methods and ultimately chooses a simpler multi-layer perceptron neural network model. Resulting in a less computationally intensive solution with higher accuracy levels. The study achieved cross-validation accuracy levels above 98 %.Chytrých domácností rychle přibývá. Inteligentní domy obvykle obsahují funkce, jako jsou hlasově aktivované funkce, automatizace, monitorování a sledování událostí. Kromě komfortu a pohodlí může integrace funkcí chytré domácnosti s metodami zpracování dat poskytnout cenné informace o pohodě rezidence chytré domácnosti. Tato studie je zaměřena na analýzu dat v inteligentních domácnostech nad rámec monitorování obsazenosti a detekce pádu osob. Jsou navrženy dva různé přístupy k integraci rozpoznávání lidské činnosti do inteligentních domácností. První přístup využívá zařízení založená na standardu KNX k získávání dat o kvalitě vzduchu v místnosti (vlhkost, CO2, teplota) a kombinování získaných dat se dvěma nositelnými zařízeními, které poskytují údaje související s pohybem. Druhý přístup zjednodušuje, zlepšuje a řeší několik nedostatků prvního přístupu, využívá různá měřicí zařízení s vyšší vzorkovací frekvencí. Zkoumá více statistických metod a nakonec volí jednodušší vícevrstvý model perceptronové neuronové sítě. Výsledkem je méně výpočetně náročné řešení s vyšší úrovní přesnosti. Studie dosáhla úrovně přesnosti křížové validace nad 98 %.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově