Smartphone and Portable Media Device: A Novel Pathway toward the Diagnostic Characterization of Human Movement

Abstract

The application of wearable and wireless systems offers the capacity to ameliorate considerable strain on medical resources. In particular the smartphone and portable media device for quantifying human movement characteristics offers the opportunity to evaluate patients in a homebound environment remote from clinical resources and postprocessing. Trial data can be easily transmitted as an email attachment with wireless connectivity to the Internet. The utility of the smartphone and portable media device has been demonstrated for quantifying gait, tendon reflex response, movement disorder, and rehabilitation exercise. Further evolution and potential has been demonstrated through the integration of machine learning to provide classification accuracy for differentiating between disparate human movement scenarios. The role of the smartphone and portable media device for quantifying human movement characteristics is further elucidated.

Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions

“…The big potential of improvement is unavoidably related to availability of the much bigger dataset than the dataset used in this work. Also these results open several questions as to the possible ways for generation and estimation of the physical load and related fatigue in addition to other available work in this domain, especially on the basis of smartphones and other gadgets [21].…”

Longitudinal Evaluation of Hemiplegic Ankle Rehabilitation Efficacy by Wearable Inertial Sensor Systems with an Assortment of Machine Learning Algorithms

“…Generally, the smartphone functioning as a wearable and wireless system for the quantification of human movement, such as gait and reflex response, involved mounting the smartphone through an elastic band, such as through a sock [1,2]. During 2016 LeMoyne and Mastroianni utilized an armband intended for securing the smartphone to the arm for mounting the smartphone as a wearable and wireless inertial sensor system near the wrist for quantifying reduced arm swing for hemiplegic gait [21].…”

“…The Python automation software consolidated the gyroscope signal data to numeric attributes. These five numeric attributes that have been previously applied for machine learning classification of ankle kinematic properties based on inertial sensor data:  Maximum of the gyroscope signal data  Minimum of the gyroscope signal data  Mean of the gyroscope signal data  Standard deviation of the gyroscope signal data  Coefficient of variation of the gyroscope signal data [1][2][3][4][5][6][7]20] A combined total of 60 instances were obtained that encompassed both the initial phase and final phase after one year of the longitudinally applied therapy regimen. The initial phase of the longitudinal study consisted of three days of recording in conjunction with a 10 second window.…”

“…Wearable and wireless systems equipped with internal sensors, such as the gyroscope, are uniquely Open Access suited for discerning the efficacy of a therapy regimen for rehabilitation [1][2][3]. For example, the smartphone with its gyroscope sensor has been demonstrated to objectively quantify the characteristics of a hemiplegic affected ankle relative to the associated unaffected ankle [4].…”

“…An advantage of the gyroscope signal acquired through an inertial sensor is the ability to record the quantified data in a manner suitable for historic retention [1][2][3][4][5][6][7]. The ramification is the ability to contrast gyroscope signal data regarding ankle dorsiflexion while engaging in a therapy regimen for rehabilitation of the hemiplegic ankle.…”