PROJECT TITLE :

MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic Segmentation - 2018

ABSTRACT:

The employment of smartphones and wearables as sensing devices has created innumerable context inference apps including a class of workout tracking apps. Workout data generated by mobile tracking apps can assist both users and physicians in achieving better health care, rehabilitation, and self-motivation. Previous approaches impose further burdens on users by requiring users to pick types of exercises or to begin/stop sessions. During this Project, we have a tendency to propose MiLift, a practical end-to-finish workout tracking system that performs automatic segmentation to remove user burdens. MiLift uses industrial off-the-shelf smartwatches to accurately and efficiently track each cardio and weightlifting workouts while not manual inputs from users. For weightlifting tracking, MiLift supports each machine-based mostly and free weight exercises, and proposes a lightweight repetition detection algorithm to ensure potency. A analysis study of twenty-two users shows that MiLift will achieve above 90 p.c average precision and recall for cardio workout classification, weightlifting session detection, and weightlifting type classification. MiLift can also count repetitions of weightlifting exercises with a median error of one.twelve reps (out of a median of nine.65). Our empirical app study on a Moto 360 watch suggests that MiLift can extend watch battery lives by up to eight.twenty five? (nineteen.13h) compared with previous approaches.


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