Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis

Abstract

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

“…Medical devices effectiveness is dependent upon several factors including the environment in which are utilised (e.g., sterilised or not, temperature, humidity, dust), the experience of the operators, maintaining, servicing and preparing the medical device 9 . For instance, recent studies demonstrated that even the positioning of applied parts (e.g., probes, sensors) in a trial might affect the significance of the results 10 . Therefore, pursuing a precision medicine approach in trialling deep learning (DL)-based medical technologies requires particular attention in the way the study is designed, executed and its results analysed [11][12][13] .…”

Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors

“…In this study, we incorporated sensors while using logistic regression as an analytical method for prediction of multiple assessment tools, also simulating experts to verify the availability of sensors. Data collection among the elderly can be very difficult [5,56], so it is not easy to strike a balance with gender restrictions. Therefore, this study discussed the judgment of falls; any discussion of gender differences was necessarily rather limited.…”

“…These clinical assessments provide an overview of the fall-risk snapshots but are usually subjective, and those who use threshold assessment scores for performing the classification are considered to be decreasing rather than increasing [4]. Therefore, previous relevant studies have used the timed up and go (TUG) time score as a clinical evaluation item to overcome the subjective interpretation of falls [5]. The objective of clinical screenings is to find the outliers.…”

“…However, the environmental assessment tools use clinical assessment scales or test data for analyzing the fall risk. The TUG assessment has been used to assess the fall risk in previous cohort studies of different versions and approaches using wearable sensors [5][6][7][8][9][10][11], predominantly by testing in controlled laboratory environments [9] or focusing on inpatients [11] and healthy subjects [5,[11][12][13]. In addition, although a few studies have focused on community-dwelling older people, there is no consensus about the scores for this group, most of which can be attributed to subjects belonging to different groups.…”

A Review of Wrist-Worn Wearable: Sensors, Models, and Challenges

“…Most WWD studies Activity studies Affect studies Figure 7: Comparison between modeling techniques used for either affect or activity recognition. [38][39][40][41]45], activity tracking [43], and home monitoring [15,49], and there is a lack of studies related to education [46], security [47], and child care.…”