Objective: Competitive exercise games are popular in areas like rehabilitation and weight loss due to their positive effects on motivation. However, it is unclear whether a human opponent is necessary, as the same benefits may be achievable with a ''human-like'' computer-controlled opponent or a human who talks to the player without playing the game. Our objective was to compare four opponent types in a competitive exercise game: a simple computer opponent, ''human-like'' computer opponent, human opponent, and a simple computer opponent accompanied by a player-selected human who chats with the player. Materials and Methods: Sixteen participants (3 women, 24.4 -7.7 years old) played a competitive arm exercise game in the above four conditions. Exercise intensity was measured with inertial sensors, and four motivation scales were measured with the Intrinsic Motivation Inventory. After playing, participants answered several questions regarding their preferences. Results: The human opponent was the favorite for 14 of 16 participants and resulted in the highest interest/ enjoyment and exercise intensity. All participants preferred the human opponent over the computer opponent accompanied by a human companion. Finally, 12 of 16 participants preferred the ''human-like'' computer opponent over the simple one. Conclusion: Our results have two implications for competitive exercise games. First, they indicate that developing computer-controlled opponents with more human-like behavior is worthwhile, but that the best results are achieved with human opponents. Second, social interaction without in-game interaction does not provide an enjoyable, intense experience. However, our results should be verified with different target populations for exercise games.

Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.

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