IMU-based smart fitness devices for weight training

Hausberger, Peter; Fernbach, Andreas; Kästner, Wolfgang

doi:10.1109/iecon.2016.7793510

Cited by 17 publications

(20 citation statements)

References 17 publications

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“…Within fitness activities, Adelsberger and Troster ( 2013 ), studied 16 participants performing a squat press, and via SVM managed to detect movements with 100% accuracy and differentiate between expert and beginner performance (94% accuracy). Research on weightlifting has used different approaches, mainly aiming at recognizing the type of exercise performed (Pernek et al, 2015 ; Hausberger et al, 2016 ; O'Reilly et al, 2017 ), or identifying performance metrics (e.g., quality of execution, intensity, deviation from a standard pattern) for each exercise (Pernek et al, 2015 ; O'Reilly et al, 2017 a,b,c). Approaches looking at performance metrics focus on the possibility of using personalized classifiers to monitor the quality of movement execution; they are more complex and demanding in terms of computational resources and sample sizes than what we presented in our study.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Preatoni

Nodari

Lopomo

2020

Front. Bioeng. Biotechnol.

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Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter-and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8-18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. Preatoni et al. Gesture Classification in Functional Training We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Preatoni

Nodari

Lopomo

2020

Front. Bioeng. Biotechnol.

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“…Sports are some of the most important parts of many people's life. It can balance the everyday life and provides the quality of life [4]. When amateurs choose a schedule for their training by themselves, they usually choose wrong training plans, because they are ignorant and they have little knowledge about their body situation.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree-Based Smart Fitness Framework in IoT

et al. 2021

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The Internet of Things (IoT) provides opportunities for improving various aspects of human life and enhancing quality of life. It enables monitoring and controlling mechanisms to be more easier than it was in the past. One of the significant objects in people's life is fitness and body activity. Using IoT and its related technologies like RFID (Radio Frequency Identification), AI (Artificial Intelligence), and WSN (Wireless Sensor Networks), can reduce cost and increase accuracy for fitness monitoring. The problem in current smart fitness and fitness monitoring systems is that there is not a complete monitoring and workout plan provider in these scenarios, especially in non-wearable sensors which are most placed on fitness devices. In this paper, an architecture is proposed that can monitor actions which is executed by sensor-embedded on fitness machines. The paper explores the implementation and examination for biceps and triceps actions. For processing data, Decision Tree Classifier (DTC) was used to reduce computational tasks and make decisions about future workout plans for each user.

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“…Recently, IMU sensors have become more widely adopted for physical activity recognition [ 9 , 10 , 11 ]. Some IMU-based systems (e.g., [ 9 ]) are used for step counting and walking detection to encourage its users to increase their ambulatory physical activity.…”

Section: Introductionmentioning

confidence: 99%

ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning

Ishii

Yokokubo

Luimula

et al. 2020

Sensors

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Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.

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IMU-based smart fitness devices for weight training

Cited by 17 publications

References 17 publications

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

A Decision Tree-Based Smart Fitness Framework in IoT

ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning

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