iPrevention

The Emerging Wearable Solutions in mHealth

“…He and Li [28] employed a combined algorithm of Fisher's discriminant ratio (FDR) criterion and J3 criterion for feature selection and hierarchical classifiers to recognize 15 activities including fall events. Majumder et al [29] applied Hjorth mobility and complexity to identify high-risk gait patterns, hence developed a fall prevention system called iPrevention.…”

“…The outcomes are often represented by four possible situations [24,30]: TP, a fall occurred and was correctly detected; FP, the system declared a fall that did not occur; true negative (TN), a fall-like event was not misclassified as a fall event; false negative (FN), a fall occurred, but the system missed it. The reliability of systems is usually evaluated based on the following parameters: sensitivity (SE) = TP/(TP+FN), which is the ratio of fallers correctly classified as fall event [27,[31][32][33][34]; specificity (SP) = TN/(TP+FN), which is the ratio of fall-like events correctly classified as nonfallers [35][36][37][38]; accuracy = (TP+TN)/(TP+FP+FN+TN), which is the ratio of true results in the whole data set [26,28,29,39]. Some works measured the performance in a different way; they utilized precision = (∩)/and recall-namely, the number of correct results divided by the total outputs-as the performance indexes [40][41][42].…”

“…Mellone et al [6] showed that a battery could power a smartphone (Samsung Galaxy S II) for 30 h with only one sensor used and 16 h with three sensors activated. Majumder et al [29] reported that a fully charged battery can only power an iPhone for 3 h at the most, when running a machine learning algorithm. Energy efficiency will continue to be an important criterion when choosing the algorithm, unless advancements in battery technology could lead to higher density energy storage.…”

Challenges in Developing Applications for Aging Populations

“…According to the United Nations Population Division statistics, at the end of 2009, the elderly population reached 737 million, accounting for 10.8% of the total worldwide population. In the year 2025, it is projected that the elderly population will account for 15% of the total population (Majumder, Rahman, Zerin, Ebel, & Ahamed, 2013a;United Nations, n.d.). As the elderly population increases and people continue to live longer, more people will require help with various aspects of daily living and disease management.…”