The correct execution of exercises, such as squats and dead-lifts, is essential to prevent various bodily injuries. Existing solutions either rely on expensive motion tracking or multiple Inertial Measurement Units (IMU) systems require an extensive set-up and individual calibration. This paper introduces a proof of concept, GymSoles, an insole prototype that provides feedback on the Centre of Pressure (CoP) at the feet to assist users with maintaining the correct body posture, while performing squats and dead-lifts. GymSoles was evaluated with 13 users in three conditions: 1) no feedback, 2) vibrotactile feedback, and 3) visual feedback. It has shown that solely providing feedback on the current CoP, results in a significantly improved body posture.

GymSoles: Improving Squats and Dead-Lifts by Visualizing the User's Center of Pressure

Smart insoles review over the last two decade: Applications, potentials, and future

This work explores the use of foot gestures for locomotion in virtual environments. Foot gestures are represented as the distribution of plantar pressure and detected by three sparsely-located sensors on each insole. The Long Short-Term Memory model is chosen as the classifier to recognize the performer's foot gesture based on the captured signals of pressure information. The trained classifier directly takes the noisy and sparse input of sensor data, and handles seven categories of foot gestures (stand, walk forward/backward, run, jump, slide left and right) without manual definition of signal features for classifying these gestures. This classifier is capable of recognizing the foot gestures, even with the existence of large sensor-specific, inter-person and intra-person variations. Results show that an accuracy of ~80% can be achieved across different users with different shoe sizes and ~85% for users with the same shoe size. A novel method, Dual-Check Till Consensus, is proposed to reduce the latency of gesture recognition from 2 seconds to 0.5 seconds and increase the accuracy to over 97%. This method offers a promising solution to achieve lower latency and higher accuracy at a minor cost of computation workload. The characteristics of high accuracy and fast classification of our method could lead to wider applications of using foot patterns for human-computer interaction.

Accurate and Fast Classification of Foot Gestures for Virtual Locomotion

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories. 

Activity Recognition Using Different Sensor Modalities and Deep Learning

Introduction: The RPM2 device (1) is a wireless, remote monitoring, pressure sensing device used for sports performance enhancement.
Methods: Participants (N=60) reported to the biomechanics laboratory for pre-testing and post-testing, as well as once each week, over a 12-week period, to perform pre-selected exercises programmed into the RPM2 device. The participants inserted the RPM2 device into their shoes prior to testing. Pre and post testing of joint range-of-motion (ROM) was measured by a goniometer, and through video analysis (age 23.10 ± 5.60 years; height 177.34 ± 6.42 cm; body mass 77.39 ± 12.55 kg). The two-mile run was used to assess the aerobic fitness and leg muscles’ endurance.
Results: A significant increase in ROM in the hip, knee, and ankle were found, when using the device and performing the exercises over a period of 12 weeks. ROM increases were observed during hip extension, hip flexion, knee flexion, and in ankle dorsiflexion and plantar flexion (>10 degrees). Adherence to exercise was high, with 95% of subjects.
Conclusion: Data suggest that using the monitoring device may lead to increases in ROM, which translates to an increase in flexibility and force production in the joint. With an increase in ROM, the increased torque produced in a given joint may lead to increases in performance.

Remote Monitoring Foot Inserts Used to Enhance Sports Performance through Increased Range of Motion

Development of a smart insole tracking system is described. Originally designed for healthcare applications, the system has found applications in both physical therapy and athletic training. The entire system is distributed between insole hardware, mobile device applications that interface with the insoles and a central Internet server for data warehousing and analysis. We describe the development of these components so far including a discussion of custom algorithm development required for the system. The athletic version has been commercialized while the more complex healthcare version is still under development.

http://www.cs.angelo.edu/~rlegrand/research/roden2014medhab.pdf

Development of a smart insole tracking system for physical therapy and athletics

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Development of a smart insole tracking system for physical therapy and athletics