The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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Preprocessing techniques for context recognition from accelerometer data

Computer based sleep recording and analysis

Learning-dependent increases in sleep spindle density have been reported during nocturnal sleep immediately after the learning session Here, we investigated experience-dependent changes in daytime sleep EEG activity after declarative learning of unrelated word pairs At weekly intervals, 13 young male volunteers spent three 24 h sessions in the laboratory under carefully controlled homeostatic and circadian conditions At approximately midday, subjects performed either one of two word-pair learning tasks or a matched nonlearning control task, in a counterbalanced order The two learning lists differed in the level of concreteness of the words used, resulting in an easier and a more difficult associative encoding condition, as confirmed by performance at immediate cued recall Subjects were then allowed to sleep for 4 h; afterward, delayed cued recall was tested Compared with the control condition, sleep EEG spectral activity in the low spindle frequency range and the density of low-frequency sleep spindles (1125-1375 Hz) were both significantly increased in the left frontal cortex after the difficult but not after the easy encoding condition Furthermore, we found positive correlations between these EEG changes during sleep and changes in memory performance between pre-nap and post-nap recall sessions These results indicate that, like during nocturnal sleep, daytime sleep EEG oscillations including spindle activity are modified after declarative learning of word pairs Furthermore, we demonstrate here that the nature of the learning material is a determinant factor for sleep-related alterations after declarative learning

https://www.zora.uzh.ch/id/eprint/49394/1/Schmidt_et_al_Encoding_difficulty_promotes_postlearning_changes_in_sleep_spindle_activity_during_napping-V.pdf

Encoding Difficulty Promotes Postlearning Changes in Sleep Spindle Activity during Napping

Our objective was to develop parameters for objective ambulatory measurements of levodopa-induced dyskinesias (LID) in patients with Parkinson's disease (PD). Twenty-three PD patients with mild to severe LID were submitted to a standardized protocol of 1-minute recordings during rest, talking, stress, and four activities of daily life (ADL). Patients were simultaneously monitored with portable multi-channel accelerometry (four pairs of bi-axial sensors mounted onto the most affected arm, leg, and at the trunk) and recorded by video. LID severity was assessed with a modified Abnormal Involuntary Movement Scale (m-AIMS). The signals were analyzed, and every 1/8-second interval the amplitude was obtained of the dominant frequency within 1–4 Hz and 4–8 Hz frequency bands (Amp1–4 and Amp4–8). For both measures, convergent validity, reproducibility, and responsiveness were determined.



In absence of voluntary movements, a significant relation was found between Amp1–4 and Amp4–8 and m-AIMS. Repeated measurements during rest showed a high reproducibility (intraclass correlation coefficient = 0.90 [Amp1–4] and 0.86[Amp4–8]). The extent to which LID increased with talking and stress correlated significantly (p = 0.02) between the objective and clinical measures (intraclass correlation for differences = 0.67). During ADL, LID occurred in a similar frequency band as voluntary movements and only Amp1–4 and Amp4–8 of the trunk and leg sensor remained highly correlated with m-AIMS.



Although objective measures of LID are reliable and responsive, they fail to distinguish LID from voluntary movements. These measures are of value only when obtained during rest (all sensor sites) or during ADL when derived from those body segments that are normally not involved in these ADL tasks (trunk and leg). Mov. Disord. 16:58–61, 2001. © 2001 Movement Disorder Society.

Accelerometric assessment of levodopa-induced dyskinesias in Parkinson's disease.

Physiological responsivity to emotional pictures in schizophrenia

This paper describes a concept for fast on-line transformation of signals into the combined time-frequency domain. Assuming a limited number of narrow-band signal components equidistantly spaced on the frequency axis, the number of computations required is minimized and an optimal time and frequency resolution is accomplished at the same time. Typically, for a postulated N-number of components, N being a power of 2, instantaneous amplitudes and frequencies are presented at each Nth input sample. From the fluctuations of the frequency over time, a measure for instantaneous bandwidth is derived that can be interpreted as the reciprocal of instantaneous signal-to-noise ratio. Instantaneous output of the FTFT may allow improved recognition of short transient patterns in noisy signals such as the Electro-Encephalo-Gram (EEG) and optimized feature coding and data compression compared to techniques, that average over a period of time. Using integer arithmetic and a high level programming language, the throughput rate on a single 20 MHz microprocessor is better than 1.5 KHz.

http://ieeexplore.ieee.org/iel5/1028/5761397/05761604.pdf

Wim L. J. Martens

Papers

The fast time frequency transform (F.T.F.T.): A novel on-line approach to the instantaneous spectrum