Toward Unobtrusive Patient Handling Activity Recognition for Injury Reduction Among At-Risk Caregivers

Abstract

Nurses regularly perform patient handling activities. These activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling activities is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is based on human observational approach, which is neither accurate nor projectable to a large population. In this paper, we aim at addressing these challenges. Our solution comprises a smart wearable device and a novel spatio-temporal warping (STW) pattern recognition framework. The wearable device, named Smart Insole 2.0, is equipped with a rich set of sensors and can provide an unobtrusive way to automatically capture the information of patient handling activities. The STW pattern recognition framework fully exploits the spatial and temporal characteristics of plantar pressure by calculating a novel warped spatio-temporal distance, to quantify the similarity for the purpose of activity recognition. To validate the effectiveness of our framework, we perform a pilot study with eight subjects, including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 91.7%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.3% and 92.5%, respectively.

Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review

“…For step detection, instrumented insoles were validated using visual observation [25,31,34,35], other devices (the Runtastic pedometer application and other smartphone applications) [34,36], or using a predefined number of steps [24,36,37] (see Table 3). To validate the instrumented insoles for posture and activity recognition, comparisons were made between the smart insole data and that collected from direct observation during data collection or from a video recording or from other wearable devices (2D accelerometer (ADXL202), gyroscope (Murata, ENC-03J), ActivPAL device, PPAC (plantar-pressure based ambulatory classification) and FF (foot force sensor) + GPS [18,26,31,32,33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60] (see Table 2).…”

Detection System for Capacitive Plantar Pressure Monitoring

“…18(a)). The external commercial ICs of References [8]- [11] can also be used to separate the input signal; however, the complexity and area of modules will be higher. V BC is used to collect the variations of the CCA controlled by the MUX through digital codes (D4-D6).…”

“…27(b). Table 17 shows the relative errors according to the following fitting curve equation: Table 18 shows the differences between Table 14 and Equation (8). The display value on the display interface is converted by an equation.…”

“…Piezoelectric sensors [8], [9] have a good AC response but a poor DC response. Nevertheless, they do not require external power supply, and they are less sensitive to temperature.…”

Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey

“…There are many promising applications for action and event recognition, such as abnormal action and event recognition in surveillance applications [1][2][3][4], interaction action and event recognition in entertainment applications [5][6][7][8], and home-based rehabilitation action and event recognition in healthcare applications [9][10][11][12], and many other analogous applications such as in [13][14][15][16][17][18]. According to the definition given by NIST [19], an event is a complex activity occurring at a specific place and time, which involves people interacting with other people and/or objects, and consists of a number of human actions, processes, and activities.…”