Designing Auditory Feedback from Wearable Weightlifting Devices

While wearable devices for fitness have gained broad popularity, most are focused on tracking general activity types rather than correcting exercise forms, which is extremely important for weightlifters. We interviewed 7 frequent gym-goers about their opinions and expectations for feedback from wearable devices for weightlifting. We describe their desired feedback, and how their expectations and concerns could be balanced in future wearable fitness technologies

Smart exercise application to improve leg function and short-term memory through game- like lunge exercises: development and evaluation

The purpose of this study was to evaluate the functionality, accuracy, and usability of a novel smart exercise application (SEA). The functionality such as counting lunges, providing task-related auditory feedback, and testing short-term memory was examined while thirteen young adults (six men, age 25.4 ± 8.3 years) performed the lunge exercise with the SEA. The accuracy of logged motion data including angles and accelerations were also tested. Another twenty-five participants (11 men, age 23.2 ± 5.7 years) evaluated the usability of the SEA interest, motivation, convenience, and strength/cognitive benefit via a questionnaire. The SEA assessed the lunge motion correctly, provided auditory feedback, and tested users’ short-term memory as required. High correlations (r = 0.90 to 0.99) with low RMSE (4.85 ̊ for direction angle, 0.13 to 0.22 m/ s2 for acceleration) were observed between the sensor output and the reference output. Bland-Altman plot also showed a low discrepancy between each of the two measures. Most participants positively answered all questions about interest (60%), motivation (40%), convenience (80%), strength benefits (92%), and cognitive benefits (88%) of the SEA. The SEA demonstrated accurate kinematic assessment of accelerations and directions, assessed the lunge motion correctly, and created the appropriate auditory feedback on the short- term memory task. The high rate of positive responses suggested the potential of the application in future use

Providing Real-Time Exercise Feedback to Patients Undergoing Physical Therapy

Musculoskeletal conditions, often requiring rehabilitation, affect one-third of the U.S. population annually. RehabBuddy is a rehabilitation assistance system that extends the reach of a physical rehabilitation specialist beyond the clinic. This thesis presents a system that uses body-worn motion sensors and a mobile application that provides the patient with assistance to ensure that home exercises are performed with the same precision as under clinical supervision. Assisted by a specialist in the clinic, the wearable sensors and user interface developed allow the capture of individualized exercises unique to the patient's physical abilities. Beyond the clinical setting, the system can assist patients by providing real-time corrective feedback to repeat these exercises through a correct and complete arc of motion for the prescribed number of repetitions. An inertial measurement unit (IMU) is used on the body part to be exercised to capture its pose. Presented is a kinematics data processing approach to defining custom exercises with flexibility in terms of where it is worn and the nature of the exercise, as well as real-time corrective feedback parameters. This thesis goes through the engineering approach, initial student investigator trials, and presents new preliminary subject data from subject trials currently ongoing at the University of Kentucky. The system is tested on multiple exercises performed by multiple subjects. It is then demonstrated how it can improve exercise adherence by assisting patients in reaching the full prescribed range of motion and avoid overextension, assist in adherence to the ideal plane of motion, and affect hold time.MSComputer Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/169159/1/Ella Reimann Final Thesis.pd

Evaluating Performance of the Lunge Exercise with Multiple and Individual Inertial Measurement Units

Pervasive Health 2016: 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, Cancun, Mexico, 16-19 May 2016The lunge is an important component of lower limb rehabilitation, strengthening and injury risk screening. Completing the movement incorrectly alters muscle activation and increases stress on knee, hip and ankle joints. This study sought to investigate whether IMUs are capable of discriminating between correct and incorrect performance of the lunge. Eighty volunteers (57 males, 23 females, age: 24.68± 4.91 years, height: 1.75± 0.094m, body mass: 76.01±13.29kg) were fitted with five IMUs positioned on the lumbar spine, thighs and shanks. They then performed the lunge exercise with correct form and 11 specific deviations from acceptable form. Features were extracted from the labelled sensor data and used to train and evaluate random-forests classifiers. The system achieved 83% accuracy, 62% sensitivity and 90% specificity in binary classification with a single sensor placed on the right thigh and 90% accuracy, 80% sensitivity and 92% specificity using five IMUs. This multi-sensor set up can detect specific deviations with 70% accuracy. These results indicate that a single IMU has the potential to differentiate between correct and incorrect lunge form and using multiple IMUs adds the possibility of identifying specific deviations a user is making when completing the lunge.Science Foundation Irelan

Estudio de sistemas inerciales en el seguimiento de terapias rehabilitadoras basadas en Machine Learning

Este trabajo ha desarrollado y caracterizado una herramienta para monitorizar ejercicios físicos de terapias pautadas empleando los datos obtenidos de cuatro unidades de medida inercial (IMUs). La monitorización incluye la identificación del ejercicio entre un catálogo y su evaluación, entre bien o mal. Dicha clasificación se ha realizado mediante algoritmos de Machine Learning. Para este fin, se optimiza la posición y el número de IMUs empleadas. Además, se determina K-Nearest Neighbours como el clasificador más adecuado y el número de IMUs óptimo en dos, una por extremidad. Con ello, se obtienen exactitudes en identificación y evaluación del 99, 5 %.This work has developed and characterized a tool to monitor physical exercises of paused therapies using the data obtained from four units of inertial measurement (IMUs). Monitoring includes identifying the exercise between a catalog and evaluating it, right or wrong. This classification was done using Machine Learning algorithms. For this purpose, the position and number of IMUs used is optimized. In addition, K-Nearest Neighbours is determined as the most suitable classifier and the optimal number of IMUs in two, one per limb. This results in accuracies in identification and evaluation of 99,5 %.Grado en Ingeniería en Tecnologías de Telecomunicació