IoT/M2M wearable-based activity-calorie monitoring and analysis for elders

Abstract

With the growth of aging population, elder care service has become an important part of the service industry of Internet of Things. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. The system uses wireless signals to estimate calorie burned by the walking and localization. In addition, it also uses wireless motion sensors to recognize physical activity, such as drinking and restroom activity. Overall, the system can be divided into four parts: wearable device, gateway, cloud server, and caregiver's android application. The algorithms we proposed for drinking activity are Decision Tree (J48) and Random Forest (RF). While for restroom activity, we proposed supervised Reduced Error Pruning (REP) Tree and Variable Order Hidden Markov Model (VOHMM). We developed a prototype service Android app to provide a life log for the recording of the activity sequence which would be useful for the caregiver to monitor elder activity and its calorie consumption.

Usability of Smartbands by the Elderly Population in the Context of Ambient Assisted Living Applications

“…The developed system uses IoT devices and xBeacon transmission techniques combined with a hybrid algorithm in order to monitor the elderly's movements and to locate items that they may have introducing the feeling of anxiety and discomfort caused by the inability to find the lost items and potentially improving their quality of life. As with the works presented in [17,18], this is an intrusive solution, given that it requires a smartphone to obtain data from the xBeacon sensors.…”

“…The work proposed by the authors in [18] suggests a solution for the recognition of some daily activities of the elderly: drinking, washing hands, urination and defecation.…”

Knowledge discovery for enabling smart Internet of Things: A survey

“…New unlabeled data can be assigned to the learned models in this learning paradigm in order to categorize (classify or predict) them according to the previously defined labels. Some examples of supervised machine learning‐based predictive tools being used in IoT are K Nearest Neighbors (KNN) (Cui, Kim, & Rosing, ), Decision Trees (Soraya et al, ), Neural Networks (Javed, Larijani, Ahmadinia, & Gibson, ), Support Vector Machines (SVM) (Fekade, Maksymyuk, Kyryk, & Jo, ), and Bayesian networks (Razafimandimby et al, ). Unsupervised : The unsupervised learning algorithms are primarily descriptive in nature and work on unlabeled data. The majority of their operations are concerned with either clustering of data or discovering patterns in them.…”

“…IoP focuses mainly on social networking and behavioral aspects of connected people and eventually, the society at large. IoH deals with the specific challenges of the health care domain and makes use of the advantages provided by IoT-faster data rate (Satija, Ramkumar, & Sabarimalai Manikandan, 2017), miniaturized sensing devices in the form of wearables (Soraya et al, 2017), privacy, and security-in the pursuit of solving these challenges. Finally, Industry 4.0 deals with the design, deployment, and management of IoT in industries and harsh industrial environments.…”