Monthly Archives: March 2026

Can human activity be reconstructed from a smartphone ?

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A recent study conducted by researchers at the Netherlands Forensic Institute and the University of Amsterdam examines whether data generated by smartphone sensors can be meaningfully exploited in a judicial context. The central question is whether motion signals recorded by these devices can be translated into a probabilistic evaluation capable of assessing whether a specific human activity occurred at a particular point in time. As everyday objects increasingly capture fine-grained behavioral traces, the methodological integration of such data into judicial reasoning has become a critical issue.

Understanding Human Activity Recognition

Human Activity Recognition, commonly referred to as HAR, is a research field that has developed over the past decade, initially within connected health, sports tracking, and smart device applications. Its principle is to use embedded smartphone sensors to identify categories of movement based on numerical signals. Most smartphones are equipped with an accelerometer and a gyroscope that continuously record variations in movement and orientation. From these time series, statistical models trained on known activities learn to recognize characteristic patterns. Walking, running, and periods of inactivity generate distinct signatures that the algorithm can compare with data extracted from a phone under examination.

In everyday use, this classification supports functions such as physical activity tracking or fall detection. In a judicial setting, however, the objective is narrower and more demanding. The issue is not simply to label a movement, but to determine what these recordings can reasonably support regarding a specific activity, and what they cannot establish.

From classification to probabilistic evaluation

In forensic science, the task is not to assert that an event occurred, but to assess the strength with which observed data support one hypothesis over an alternative. Outputs from activity recognition models can be incorporated into this framework by expressing their evidential value in the form of a likelihood ratio. In practical terms, this involves evaluating whether the recorded sequence is more probable under one clearly defined scenario than under another. The algorithm does not decide that a person was running or walking. It provides a comparative measure of how strongly the data support one of the competing hypotheses.

Methodological illustration in a judicial context

Consider a smartphone seized in the course of an assault investigation. At a time considered critical to the events, the motion sensors recorded a sequence of data.

Two hypotheses are formulated. Under the first, the individual was walking normally. Under the second, the individual was running. The analysis indicates that the observed signals are ten times more probable under the running hypothesis than under the walking hypothesis. The likelihood ratio is therefore ten in favor of the proposition that the person was running. This result does not constitute direct proof of running. It expresses the relative probabilistic support that the data provide for that hypothesis compared with the alternative. The evidential weight of this information within the case file will depend on the remaining evidence and on the overall coherence of the competing scenarios.

The principal advantage of this approach lies in its compatibility with the Bayesian logic already recognized in the evaluation of scientific evidence.

Scientific and methodological limitations

Human Activity Recognition models are typically trained on datasets collected under controlled conditions. In real-world situations, however, numerous factors influence the recorded signal. The way the phone is carried, whether in a pocket, a bag, or held in the hand, the nature of the ground surface, the individual’s emotional state, and the device’s software configuration may all affect the measurements. Acute stress, for example, can alter the amplitude and regularity of movements. Similarly, a software update may modify the sampling frequency or the filtering parameters applied to sensor data. These technical aspects, often invisible to users, directly shape the structure of the data being analyzed.

Inter-individual variability must also be considered. Two individuals running at comparable speeds may nevertheless produce slightly different motion signatures.

In addition, data integrity must be verified. Sensor logs may be incomplete, altered, or disabled depending on device settings. As with any algorithmic method, independent validation, knowledge of error rates, and transparency regarding the model used are essential prerequisites for admissibility in a judicial context.

From source to activity, a parallel with Forensic Genetics

These findings echo a distinction that has become central in forensic genetics, namely the difference between the source level and the activity level.

In our article “Cigarette Butts: Can a Simple Kiss Distort DNA Interpretation?”, we emphasized that identifying the origin of a genetic profile does not, by itself, explain how it was deposited. The same reasoning applies here. Demonstrating that a motion pattern is compatible with running does not, on its own, establish the precise circumstances of the action. In both contexts, the key issue is identical. Moving from a technical observation to an interpretation at the activity level requires explicit scenario comparison and rigorous probabilistic evaluation. Data extracted from a smartphone, like DNA profile data, acquire meaning only within this structured analytical framework.

For judges and forensic experts, the primary challenge lies less in the technology itself than in understanding its methodological foundations. Producing a likelihood ratio requires clearly defined competing hypotheses, documented model validation, and explicit acknowledgment of analytical limitations. A smartphone does not become an automatic witness to events. Under strict conditions, however, it may provide probabilistic information capable of informing one scenario among several. The central issue is therefore not the mere availability of digital data, but the scientific robustness and legal relevance of their interpretation.

Source :

McCarthy, C., van Zandwijk, J. P., Worring, M., & Geradts, Z. (2025). Forensic Activity Classification Using Digital Traces from iPhones: A Machine Learning-based Approach.
Disponible en ligne : https://arxiv.org/abs/2512.03786

Quand un baiser permet de transférer l'ADN d'une tierce personne sur un mégot de cigarette. Forenseek

Cigarette butts: can a simple kiss mislead DNA interpretation?

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A classic crime scene issue

Cigarette butts represent a major biological substrate in forensic casework. Rich in epithelial cells deposited through saliva, they generally yield exploitable genetic profiles. In practice, the discovery of a DNA mixture on a cigarette filter is often interpreted as evidence that two individuals smoked the same cigarette or handled it in close succession. However, with the increasing sensitivity of modern quantification and STR amplification techniques, laboratories are now capable of detecting minute amounts of DNA, including those resulting from indirect transfers. The question is therefore no longer simply “Whose profile is this?”, but rather “How did this DNA get there?”

An experimental protocol based on two realistic scenarios

The authors of this pilot study tested two distinct configurations.

First scenario: kiss, then cigarette.
A couple exchanged a deep kiss involving saliva transfer. Each partner then smoked a cigarette at different intervals: immediately, and then 5, 15, 30, 60, 90, and 120 minutes after contact. The objective was to assess whether the partner’s DNA, retained in the oral cavity, could be secondarily transferred onto the cigarette filter.

Second scenario: shared cigarette.
Both partners alternately smoked the same cigarette, reproducing a case of direct co-consumption. Samples were analyzed either immediately after collection or after a 30-day storage period in order to evaluate the impact of time on DNA quantity and quality.

Detectable persistence for up to two hours

In the “kiss then cigarette” scenario, alleles attributable to the non-smoking partner were detected on cigarette butts up to 120 minutes after contact. The quantity of transferred DNA gradually decreased over time but remained detectable under several experimental conditions. In other words, the presence of a minor DNA profile on a cigarette butt does not, by itself, demonstrate that two individuals smoked the cigarette. A prior intimate contact could be sufficient to explain the mixture. These findings align with previous research showing that salivary DNA can persist in the oral cavity for several tens of minutes, or longer, depending on individual and physiological factors.

The determining effect of processing delay

The study also demonstrated a significant decrease in total DNA quantity after 30 days of storage, accompanied by an increase in the degradation index. This phenomenon particularly affects the minor component of the mixture, which is more fragile and more prone to partial loss (allelic drop-out, imbalance). In practical terms, a cigarette butt analyzed promptly may reveal a detectable mixture, whereas delayed processing could result in an apparently single-source profile.

Such temporal variability complicates interpretation and underscores the importance of carefully documenting storage conditions and processing timelines.

The contribution and limits of Y-STR markers

In cases where the female partner smoked after the kiss, Y-STR analyses allowed specific monitoring of the transferred male component. Complete Y profiles were obtained up to one hour after contact, with alleles still detectable at two hours under certain conditions. However, progressive degradation and low template quantities again require caution and contextual interpretation.

Interpreting at the activity level is essential

These findings clearly illustrate the now central distinction between:

  • Source level: whose DNA is it?
  • Activity level: by what mechanism was it deposited?

When a mixture is detected on a cigarette butt recovered from a crime scene, several scientifically plausible scenarios may exist: co-consumption, successive handling, secondary transfer following prior intimate contact, or even a combination of these hypotheses. An expert cannot therefore limit their assessment to identifying the DNA contributors. They must also evaluate deposition mechanisms consistent with current scientific knowledge, taking into account secondary transfer dynamics and the impact of time.

Conclusion

This experimental study, conducted under controlled conditions, does not claim to establish a universal rule applicable to all judicial situations. It does, however, clearly demonstrate that secondary oral transfer onto a cigarette butt is possible and may remain detectable for up to two hours after a simple kiss. At a time when the sensitivity of genetic analysis techniques continues to increase, these results reaffirm a fundamental principle of forensic science: detecting DNA is not, in itself, proof of a particular scenario. Interpretation must be rigorous, contextualized, and grounded in activity-level reasoning in order to avoid overinterpretation before the courts..

Source :

GIANFREDA, Denise, CORRADINI, Beatrice, FERRI, Gianmarco, FERRARI, Francesca, BORCIANI, Ilaria, CECCHI, Rossana, SANTUNIONE, Anna Laura. Preliminary study of mixed traces on cigarette butts and non-self DNA transfer, persistence, prevalence and recovery in different forensic scenarios. Legal Medicine, 2026, vol. 81, article 102803. DOI: 10.1016/j.legalmed.2026.102803.