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Through this comparison, the Dynamic Ameva Classification System has been applied, implementing a dynamic sampling rate method for energy reduction. The frequency varies from 32 Hz for sitting, standing and lying to 50 Hz for walking, upstairs and downstairs. It must be taken into account that these frequencies were obtained experimentally by studying the pattern of the different activities from the point of view of the accelerometery. This study was conducted covering over 600,000 different time windows of activities obtained from 10 different users. To respect the heterogeneity of the target users of the proposed recognition system, users with Selleck Nutlin 3 different profiles were selected, from those 21 years of age with an athletic profile, to seniors 82 years of age with a sedentary profile. Thanks to this information, it is possible to determine a suitable accelerometer frequency spectrum according to each activity. It should be noted that this frequency will have not only a power impact due to the decrease of data obtained from the accelerometer, but will also cause a reduction of the execution time of the algorithm, due to the smaller size of the time windows. Algorithm 1 calculates the sampling rate corresponding to a recognized activity at a given time. For the calculation, the current recognized activity and previous detected activity are taken into account. If the recognition process enters into a stable phase, i.e., there is continuity in the last set of recognized activities, the algorithm proceeds to update the sample rate. This update is TAK-875 mouse calculated from the sampling rate associated with the activity detected, which is obtained from the map SampleRateList. As mentioned before, the specific values for the list SampleRateList are calculated experimentally for each of the activities recognized. Later, if stabilization occurs for a prolonged period, our proposal proceeds to the regular updating of the sample rate based on the base log2 of the number of memories used. Algorithm 1 Dynamic sampling rate algorithm. ?SampleRateList �� InitSampleRate(Frequencies) ?MaxMemoryList �� InitMemoryList(Memories) ?AmevaIsRunning �� true ?Count �� 0 ?WinSize �� 5 ?WinSamples �� 50 * WinSize; ?Aprevious �� GetAmevaActivity(Statistics) ?while AmevaIsRunning do ??Alast �� GetAmevaActivity(Statistics) Adenylyl cyclase ??if Alast == Aprevious then ???Count �� ActivityMemory + 1 ??else ???Count �� 0 ??end if ??if IsCriticalActivity(Alast) and ??Count > GetMaxMemory(Alast) then ???NewFrequency �� SampleRateList(Alast) ???WinSamples �� NewFrequency * WinSize ??end if ??if Count%30 == MaxMemoryList(Alast) then ???WinSize �� WinSize + log2(Count%30) ???WinSamples �� NewFrequency * WinSize ??end if ??Aprevious �� Alast ?end while The maximum memory limit is defined as the specific period for which an activity is considered stable. This maximum memory is fixed experimentally and depends on the specific activity.