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Activity monitoring is important for assessing daily living conditions for elderly patients and those with chronic diseases. Transitions between activities can present characteristic patterns that may be indicative of quality of movement. To detect and analyze transitional activities, a manifold-based approach is proposed in this paper. The proposed method uses a recursive spectral graph-partitioning algorithm to segment transitions in activity. These segments are subsequently mapped to a reference manifold space. Categorization of transitions is performed with the corresponding features in the manifold space. The practical value of the work is demonstrated through data collected under laboratory conditions, as well as patients recovering from total knee replacement operations, demonstrating specific transitions and motion impairment compared to normal subjects.