Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2022 Mar 22;22(7):2446. doi: 10.3390/s22072446.
We present here a method for recognising dance moves in sequences using 3D accelerometer and gyroscope signals, acquired by a single wearable device, attached to the dancer's leg. The recognition entails dance tempo estimation, temporal scaling, a wearable device orientation-invariant coordinate system transformation, and, finally, sliding correlation-based template matching. The recognition is independent of the orientation of the wearable device and the tempo of dancing, which promotes the usability of the method in a wide range of everyday application scenarios. For experimental validation, we considered the versatile repertoire of solo jazz dance moves. We created a database of 15 authentic solo jazz template moves using the performances of a professional dancer dancing at 120 bpm. We analysed 36 new dance sequences, performed by the professional and five recreational dancers, following six dance tempos, ranging from 120 bpm to 220 bpm with 20 bpm increment steps. The recognition scores, obtained cumulatively for all moves for different tempos, ranged from 0.87 to 0.98. The results indicate that the presented method can be used to recognise repeated dance moves and to assess the dancer's consistency in performance. In addition, the results confirm the potential of using the presented method to recognise imitated dance moves, supporting the learning process.
我们在此提出了一种使用 3D 加速度计和陀螺仪信号识别舞蹈动作的方法，这些信号由单个可穿戴设备采集，设备附着在舞者的腿部。识别过程包括舞蹈节奏估计、时间缩放、可穿戴设备的方向不变坐标系统转换，以及最后基于滑动相关的模板匹配。该识别方法不依赖于可穿戴设备的方向和舞蹈节奏，这提高了该方法在广泛的日常应用场景中的可用性。为了进行实验验证，我们考虑了多种独舞爵士舞蹈动作。我们使用一名专业舞者以 120 bpm 的速度表演的 15 个真实独舞爵士模板动作创建了一个数据库。我们分析了 36 个新的舞蹈序列，由专业舞者和五名休闲舞者在六个不同的节奏（从 120 bpm 到 220 bpm，每 20 bpm 增加一个步长）下表演。对于不同节奏的所有动作，识别的累积分数从 0.87 到 0.98 不等。结果表明，所提出的方法可用于识别重复的舞蹈动作，并评估舞者在表演中的一致性。此外，结果还证实了使用所提出的方法识别模仿舞蹈动作的潜力，从而支持学习过程。
