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# MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living

Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz

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Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz. MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living. 2025. hal-05048859

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# MURAL: A MULTI-RESIDENT AMBIENT SENSOR DATASET
## ANNOTATED WITH NATURAL LANGUAGE FOR ACTIVITIES OF DAILY LIVING

AUTHOR VERSION

Xi Chen¹,², Julien Cumin¹, Fano Ramparany¹, Dominique Vaufreydaz², ∗

¹ Orange Innovation

² Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France

Contact email for MuRAL access: xi.chen@orange.com

ABSTRACT

Recent advances in Large Language Models (LLMs) have shown promising potential for human activity recognition (HAR) using ambient sensors, especially through natural language reasoning and zero-shot learning. However, existing datasets such as CASAS, ARAS, and MARBLE were not originally designed with LLMs in mind and therefore lack the contextual richness, complexity, and annotation granularity required to fully exploit LLM capabilities. In this paper, we introduce MuRAL, the first Multi-Resident Ambient sensor dataset with natural Language, comprising over 21 hours of multi-user sensor data collected from 21 sessions in a smart-home environment. MuRAL is annotated with fine-grained natural language descriptions, resident identities, and high-level activity labels, all situated in dynamic, realistic multi-resident settings. We benchmark MuRAL using state-of-the-art LLMs for three core tasks: subject assignment, action description, and activity classification. Our results demonstrate that while LLMs can provide rich semantic interpretations of ambient data, current models still face challenges in handling multi-user ambiguity and under-specified sensor contexts. We release MuRAL to support future research on LLM-powered, explainable, and socially aware activity understanding in smart environments. For access to the dataset, please reach out to us via the provided contact information. A direct link for dataset retrieval will be made available at this location in due course.

**Keywords:** Human Activity Recognition · Dataset · Large Language Model · Smart Home · IoT.

# 1 Introduction

The recognition of Activities of Daily Living (ADL) in smart-home environments plays a crucial role in various applications such as healthcare [1, 15], security [10, 21], and home automation [19]. The recognition of Activities of Daily Living (ADL) in smart-home environments plays a crucial role in applications such as healthcare, security, and home automation. Compared to camera-based approaches, ambient sensors of-

fer advantages in terms of privacy, energy efficiency, and non-intrusiveness. Unlike wearable sensors, they do not require users to wear devices continuously, making them more comfortable and user-friendly. These properties have driven increasing interest in ambient-sensor-based ADL recognition in recent years.

A unique challenge of ambient sensor data lies in its low-level, discrete nature—for example, a door sensor typically outputs only "open" or "closed" states. However, the semantic meaning of such readings is highly context-dependent: opening a cabinet, a room door, or a front door may imply very different intentions. This makes ambient sensors particularly amenable to interpretation by large language models (LLMs), which can infer higher-level meaning when provided with rich natural language descriptions of sensor context. As a result, recent studies have explored LLM-based approaches for interpreting ambient sensor data through zero-shot reasoning.

Leveraging their powerful in-context learning abilities, LLMs have recently achieved significant breakthroughs in ambient-sensor-based ADL recognition. Most studies [6, 7, 12, 16, 17, 20, 22] formulate sensor inputs as natural language prompts, enabling LLMs to recognize activities under zero-shot or few-shot settings in single-resident scenarios. Chen et al. [5] extended this paradigm to multi-resident environments, using chain-of-thought (CoT) [23] reasoning to disambiguate mixed sensor streams and provide fine-grained descriptions for each resident. Other works exploit LLMs for activity generation [18, 24], explainability [11], or even raise privacy concerns [13] in smart home contexts.

However, most existing studies in this field still rely on traditional benchmark datasets that were developed prior to the emergence of LLM, such as CASAS [8], ARAS [2], and MARBLE [3]. While these datasets have played a crucial role in advancing ambient-based ADL recognition over the past decade, they exhibit several limitations when applied in the context of LLM-based approaches:

* Lack of contextual richness: traditional datasets typically lack rich contextual metadata, including detailed descrip-
<figure><img src="image_2.png" /><figcaption>**Figure 1: DOMUS Intelligent Apartment testbed.**</figcaption></figure>

tions of sensor placement, spatial layouts, and the semantic
roles of rooms and objects. This limitation restricts the abil-
ity of LLM to fully utilize their strengths in understanding
natural language descriptions and leveraging common-sense
reasoning based on context.

* Low environmental complexity: with the increasing model-
  ing capabilities of LLM and multimodal reasoning frame-
  works, there is a growing demand for more complex and
  realistic smart-home environments. However, most existing
  datasets are collected in relatively simple settings, often with
  only one or two residents, and scripted activity routines, thus
  failing to capture the dynamic and ambiguous situations that
  LLM are capable of handling.

* Coarse annotation granularity: Most datasets focus on high-level activity classification labels, which sufficed for traditional supervised learning approaches. In the era of LLM, however, there is a shift towards fine-grained reasoning tasks such as fine-grained action attribution, activity generation, and natural language understanding—all of which require richer and more structured annotations.

These limitations collectively hinder the full exploration of
the potential of LLM for ambient-based ADL recognition.
Without richer context, more diverse and challenging environ-
ments, and finer-grained labels, current datasets fall short of
supporting the next generation of intelligent, explainable, and
generalizable activity recognition systems driven by large lan-
guage models.

To bridge this gap, we introduce **MuRAL**, a **Multi-Resident Ambient** sensor dataset with natural **Language**, specifically designed to unlock the full reasoning capabilities of LLMs for

smart-home activity understanding. MuRAL introduces sev-
eral key innovations that make it particularly well-suited for
exploring the potential of LLM in context-rich ADL recogni-
tion tasks:

* Fine-grained natural language annotations: Beyond conventional labeling of each sensor event with the corresponding resident identity and activity category, MuRAL provides natural language descriptions of the resident's fine-grained actions and contextual state at the moment the event is triggered.

* Context-rich session-level descriptions: For every session, MuRAL provides structured natural language descriptions of the scenario, including the roles of each resident (e.g., parent, child, roommate) and the real-world context of the session (e.g., "preparing for a shared dinner", "welcoming a visitor", "cleaning after a meal"). These session-level annotations support higher-level reasoning and provide LLM with the narrative structure required for understanding multi-resident behavior in daily life.

* Complex and dynamic multi-resident scenarios: The data was collected in a realistic environment designed to emulate everyday living. The setting involves 1 to 4 residents simultaneously present, with the number of occupants changing dynamically as individuals enter and leave the environment. The dataset captures a wide range of multi-person interactions, including collaborative activities, co-located but independent behaviors, and transitions between individual and group tasks. These characteristics make MuRAL particularly valuable for modeling ambiguous, overlapping, and so-
cially situated activities, challenges that are rarely addressed
in existing datasets.

In summary, this paper makes the following contributions:

1. We introduce MuRAL, a multi-resident ambient sensor dataset with natural language annotations tailored for LLM-based HAR.

2. We describe its design principles, data collection methodology, and annotation pipeline.

3. We benchmark the dataset using state-of-the-art LLMs on three core tasks: subject assignment, action description, and activity classification, uncovering key challenges and insights.

We believe MuRAL offers a valuable foundation for the next generation of explainable, language-driven activity recognition systems.

## 2 Related Work

Due to the complexity and high cost associated with the collection, annotation and use of multi-person environmental sensor data, multi-person datasets are much scarcer than single-person datasets [4, 9]. The most widely used datasets are CASAS [8], ARAS [2] and MARBLE [3].

CASAS is a comprehensive collection of datasets that includes both single-person and multi-person subsets, totaling over 89 datasets. These datasets were collected from more than 50 distinct environments, with each subset recorded using between 20 and 86 sensors of various types. Most CASAS subsets focus on single-person scenarios, and only some of them involve multiple persons, and annotations often lack details in these multi-person datasets. The well-annotated multi-person datasets typically involve only two users in a given environment. One notable feature of CASAS is its use of ceiling-mounted motion sensors to track individual users' local positions. Additionally, door sensors, item contact sensors, and other complementary sensors provide more comprehensive activity information. However, the ceiling-mounted nature of motion sensors makes it difficult to describe their positions in natural language, as they lack distinguishing spatial references. This limitation presents challenges for LLM-based reasoning, as spatial semantics play a crucial role in activity recognition and user localization. To facilitate user-specific activity recognition, CASAS multi-person datasets generally provide activity labels corresponding to each sensor event, with most activities being associated with a specific individual. Some datasets further include detailed annotations that explicitly specify which user triggered each event. These annotations are particularly valuable for training and evaluating sensor event separation models, as they provide essential ground truth for distinguishing user-specific activities.

The ARAS dataset is a multi-person dataset collected over one month of real-life activities by two pairs of users in two different real homes. It utilizes a variety of environmental sensors, including photocell, temperature, distance, force, and contact sensors. One of its key advantages is its continuous data

collection in real-world environments over an entire month, which is exceptionally rare among publicly available datasets. Moreover, unlike CASAS, ARAS sensors have well-defined contextual information, including their location, function, and type, all of which can be described in natural language. As a result, ARAS was one of the earliest datasets explored for applying large language models to sensor-based activity recognition [5, 12]. However, ARAS provides annotations for 27 activity categories but does not specify which individual user triggered each event. This limitation reduces its suitability for evaluating models that track individual users. Furthermore, ARAS only supports two simultaneous users, limiting its applicability to more complex multi-person household scenarios.

Compared to CASAS and ARAS, MARBLE is a more recently developed dataset explicitly designed to capture richer and more diverse multi-user interactions. It contains sensor data collected in environments with 1 to 4 users present simultaneously. Unlike CASAS and ARAS, MARBLE integrates both environmental sensors and wearable devices, including smartwatches, smartphones, and micro-localization systems, allowing for more precise user tracking. Moreover, MARBLE's environmental sensors, similar to those in ARAS, have well-defined contextual information, including location, function, and type, all of which can be described in natural language. MARBLE provides fully annotated environmental sensor data, specifying both the identity of the triggering user and their corresponding activity, addressing a key limitation in CASAS and ARAS. The diversity of user numbers, the rich sensor context, and the completeness of MARBLE's annotations make it highly suitable for training and evaluating models in multi-user activity recognition, sensor event separation, and user tracking. Unfortunately, the activities collected in the MARBLE dataset are scripted and do not align with real-world activity timelines. Typically, each activity, such as cooking or watching TV, lasts only a few minutes, introducing a bias between the dataset and real-world activity distributions. The scripted nature and short activity durations (often a few minutes) significantly deviate from real-world behavior patterns, limiting the dataset's generalizability for long-form reasoning tasks.

In contrast, MuRAL is designed to capture long-form, organically flowing multi-resident interactions, annotated at both the action and activity level, with contextual natural language that fully supports LLM-based reasoning.

## 3 MuRAL Dataset

In this section, we present the details of the MuRAL dataset, outlining the methodology of data collection and annotation, the experimental setup and the implementation process. For access to the dataset, please reach out to us via the provided contact information. A direct link for dataset retrieval will be made available at this location in due course.

### 3.1 Methodology

Before explaining the specific processes of data collection and annotation, we first outline the characteristics we designed
for this dataset, considering multi-user scenarios and potential LLM applications. The selection, configuration, deployment of sensors, as well as the design and execution of experimental scenarios, are all aimed at endowing the dataset with these characteristics as much as possible.

### 3.1.1 Spatial Separability

In multi-user scenarios, user ambiguity is one of the primary challenges in context recognition. When multiple users fall within the sensing range of the same sensor, the sensor's user specificity decreases, making it difficult for models to accurately determine the number of users and their identities. To mitigate this issue, we prioritize spatial separability when designing the experimental environment, aiming to minimize instances where multiple users trigger the same sensor.

Specifically, we adjust the sensing range of motion sensors (e.g., infrared motion sensors) to confine their coverage to specific single-user activity zones, such as a sink, thereby reducing the likelihood of detecting multiple users simultaneously. Additionally, we avoid using sensors that capture activities across large areas, such as ceiling-mounted motion detectors that monitor an entire living room or temperature sensors that measure ambient conditions over a broad space. This design enhances the spatial resolution of the data and improves the dataset's robustness in multi-user scenarios. While this approach may increase the density of sensor deployment, it effectively reduces data ambiguity and improves recognition performance in downstream models.

### 3.1.2 Temporal Separability

In multi-user scenarios, user ambiguity can arise if a sensor fails to promptly detect a user switch. For example, if User A finishes washing their hands and leaves, followed immediately by User B, a motion sensor at the sink that does not register this transition may merge their activities into a single sequence. To mitigate this, we ensure our dataset exhibits temporal separability by increasing the sampling frequency of motion sensors to more than 1Hz. This allows the sensor to emit a deactivation event within 1 second of inactivity, ensuring that user transitions with at least a 1-second gap are accurately recorded.

### 3.1.3 Semantic Maximization

To enhance the dataset's interpretability for LLM while minimizing the redundant data and reducing computational overhead, each sensor should maximally represent meaningful contextual information such as location, actions and activities. For example, smart plugs monitor the real-time power consumption of appliances (e.g., TVs, induction stoves) to infer user interactions. To further enhance semantic clarity, we process data through eventization and discretization, aligning each event with specific actions. For instance, we record a "TV on" event when its power exceeds a predefined threshold and a "TV off" event when it drops below another threshold.

### 3.1.4 Realistic Distribution

To enable LLM to handle the complexity of real-world daily activities, data distribution should closely resemble reality. This requires that scene design, role settings, interactions, activity sequences, and durations in data collection align with real-world patterns. In our data collection process, we only provide participants with high-level contextual information, such as their household role, preset time frame (e.g., morning/evening, weekday/weekend), and possible activities, allowing them to act naturally according to their preferences and habits. While personal hygiene activities (e.g., washing up) are simulated for privacy reasons, activities such as cooking and dining occur naturally, ensuring a more authentic dataset.

## 3.2 Environmental Setup

The MuRAL dataset was collected in the DOMUS<fnref n="1" /> Intelligent Apartment testbed, a 63 m<fnref n="2" /> living lab designed to simulate real-world residential environments. As shown in Figure 2, the apartment includes a kitchen (with a coffee maker, induction stove, cabinets, sink, countertop, microwave, and refrigerator), a dining area (with a dining table, chairs, and a snack cabinet), a living room (with a couch, coffee table, TV, gaming console, and bookshelf), a bathroom (with a shower, toilet, and sink), as well as one double and one single bedroom.

The apartment is equipped with 23 sensors (Figure 2), including 6 infrared motion sensors (Fibaro FGMS001², AEON ZW100³), 10 magnetic contact sensors (Philio PSM02⁴), and 6 smart plugs (Fibaro FGWP102⁵) for monitoring appliance power consumption. To protect privacy, no sensors were placed inside bedrooms, making bedroom activities outside the primary scope of our study.

Due to the lack of pressure sensors, we employed alternative sensing methods to infer interactions with certain furniture. For example, we placed magnetic sensors between the dining table and chairs to detect whether a chair was in use, requiring participants to return the chair to its original position after use to reset the sensor. Similarly, we installed a magnetic sensor on the toilet lid to determine whether it was open or closed, instructing participants to lower the lid after each use to ensure consistent event recording. For couch activity detection, we positioned an infrared motion sensor on the coffee table and limited its detection range to the couch area, allowing us to infer whether someone was present. While these adaptations enable activity recognition, they may introduce slight deviations from natural behavior, which should be considered when using the dataset.

<footnote n="1">¹https://www.liglab.fr/fr/recherche/plateformes/domus</footnote>

<footnote n="2">²https://www.openhab.org/addons/bindings/zwave/thing.html?manufacturer=fibaro&file=fgms001_3_2.html</footnote>

<footnote n="3">³https://www.openhab.org/addons/bindings/zwave/thing.html?manufacturer=aeon&file=zw100_0_0.html</footnote>

<footnote n="4">⁴https://www.openhab.org/addons/bindings/zwave/thing.html?manufacturer=philio&file=psm02_0_0.html</footnote>

<footnote n="5">⁵https://www.openhab.org/addons/bindings/zwave/thing.html?manufacturer=fibaro&file=fgwp102_3_2.html</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Floor plan of the DOMUS smart apartment testbed with the names and locations of all ambient sensors.</figcaption></figure>

DOMUS uses the *OpenHAB platform*<fnref n="6" /> to integrate, control, and collect all sensor data. Additionally, during the experiments, we used the six ceiling-mounted cameras and microphones pre-installed in the common areas of the apartment to record video and audio for annotation purposes. The video recordings were used solely for annotation purposes and were permanently deleted once the labeling process was complete, in full compliance with GDPR.

## 3.3 Data Collection

MuRAL includes data collected from 18 volunteer participants. To ensure privacy and fairness, no information regarding participants' age, occupation, nationality, or gender was recorded, selected, or analyzed. Volunteers were recruited via internal university mailing lists, making it reasonable to assume that most participants were students, researchers, or educators, while other demographic attributes remained diverse. Similarly, no explicit identifiers were recorded while data collection,

and re-identifying individuals from the dataset is considered to be impossible.

Participants were scheduled based on their self-selected time slots, with each session consisting of 2 to 4 people. Across the 21 sessions, only one session included 2 participants, one had 4 participants, while the remaining 19 had 3 participants. Before data collection, we provided an overview of the apartment layout, explained the functioning of facilities and appliances, and allowed 10 to 20 minutes for participants to familiarize themselves with the environment. Each participant was assigned a social role (e.g., family members, spouses, roommates) and was given a predefined context to simulate, such as a weekday morning, a weekday evening, or a weekend afternoon. Beyond these basic instructions, participants were free to engage in activities according to their preferences and habits. These minimal scenario constraints encourage spontaneous and diverse interactions, while still situating the activities in relatable, everyday contexts.

To support realistic and natural activities, we stocked the refrigerator with various ingredients, including pasta, eggs,

<footnote n="6">⁶https://www.openhab.org/</footnote>
<table><caption>**Table 1: Activity ID and labels of the MuRAL Dataset.**</caption><tr><td>0. others</td><td>1. using toilet</td><td>2. preparing drinks</td><td>3. preparing breakfast</td><td>4. having breakfast</td></tr><tr><td>5. grabbing food or drinks</td><td>6. bedroom personal activity</td><td>7. grabbing tableware</td><td>8. clearing table</td><td>9. watching TV</td></tr><tr><td>10. drinking water, coffee or dinks</td><td>11. cleaning apartment</td><td>12. taking shower</td><td>13. washing dishes</td><td>14. getting out of the apartment</td></tr><tr><td>15. coming into the apartment</td><td>16. reading books</td><td>17. playing video games</td><td>18. resting on the couch or the chairs</td><td>19. preparing dinner</td></tr><tr><td>20. having dinner</td><td>21. chatting</td><td>22. playing board games</td><td>23. treating fruits</td><td>24. having fruits or snacks</td></tr><tr><td>25. personal washing</td><td>26. using pc</td><td></td><td></td><td></td></tr></table>

<figure><img src="image_4.png" /><figcaption>**Figure 3: Activity timeline for multiple residents in Session 01 of MuRAL.**</figcaption></figure>

salad, microwave meals, fruits, and ice cream. The snack cabinet contained chips, cookies, and chocolate. The bookshelf provided a selection of books. Additionally, the gaming console was equipped with a range of popular casual games, allowing participants to engage in different types of leisure activities. This setup ensured that participants could perform naturalistic sequences of daily activities—such as cooking, reading, or relaxing—thereby generating sensor events grounded in genuine user intentions. For privacy and hygiene reasons, however, personal care activities such as showering, brushing teeth, and using the toilet were simulated by participants rather than performed in full.

To reflect natural social dynamics, participants were free to enter or leave the apartment during the session according to the progression of the scenario. As a result, the number of active residents varied over time. Each experiment lasted approximately one hour. This duration was sufficient to capture multiple overlapping activities and transitions, providing rich temporal sequences for downstream reasoning.

## 3.4 Data Annotation

Unlike previous datasets, MuRAL provides fine-grained natural language descriptions of each user's actions within long, complex multi-user scenarios, significantly increasing the annotation difficulty. To ensure accuracy and consistency, all annotations were completed by a single annotator based on the recorded video footage from each session.

### 3.4.1 Subject Identification and Action Description

For each session, the annotator assigned user identifiers sequentially based on the order of sensor events. The resident who triggered the first event was assigned an identifier “A”, the next distinct resident who triggered a non-A event was as-

signed “B”, and so on. For each sensor event, the annotator first identified the corresponding resident identifier, then wrote a natural language description of the resident’s action and context at the time of the trigger.

For example, a “kitchen cabinet door CLOSED” event might be annotated as: “A takes out dishes from the kitchen cabinet, moves them to the dining room, and places the dishes on the table.”

The same type of event may correspond to diverse behaviors depending on the scene, context, and resident, making semantic inference based on user identity and context a core challenge for models working with MuRAL.

### 3.4.2 Activity Labeling

In addition to fine-grained action descriptions, we also provide higher-level activity labels, similar to previous work, enabling the dataset to support activity recognition classification tasks. After completing the identity and action annotations, the annotator grouped events by resident identifier and labeled each resident’s sequence with one of 27 predefined activity categories, based on both their action descriptions and the recorded video. These activities are listed and presented in Table 1. Figure 3 provides an example of an activity timeline depicted based on these annotations.

## 4 Statistical Analysis

We conduct a statistical analysis of the MuRAL dataset to better present its structure and showcase the properties that make it suitable for multi-resident activity recognition with natural language supervision. Table 2 summarizes the statistics across 21 recording sessions.
<table><caption>**Table 2: Statistics of the MuRAL Dataset.**</caption><thead><tr><th>Session</th><th>#Users</th><th>#Events</th><th>#Sensors</th><th>#Description</th><th>Description Length</th><th>#Activity</th><th>#Types of Activity</th><th>Duration per Activity (min)</th></tr></thead><tbody><tr><td>mean</td><td>3.00</td><td>406.81</td><td>18.76</td><td>200.33</td><td>11.67</td><td>41.71</td><td>15.71</td><td>3.75</td></tr><tr><td>std</td><td>0.31</td><td>72.86</td><td>2.18</td><td>53.57</td><td>0.67</td><td>10.96</td><td>2.21</td><td>1.35</td></tr><tr><td>min</td><td>2</td><td>266</td><td>15</td><td>128</td><td>9.84</td><td>20</td><td>10</td><td>1.98</td></tr><tr><td>max</td><td>4</td><td>558</td><td>22</td><td>284</td><td>13.22</td><td>63</td><td>19</td><td>7.01</td></tr><tr><td>01</td><td>3</td><td>394</td><td>21</td><td>260</td><td><strong>9.84</strong></td><td><strong>63</strong></td><td><strong>19</strong></td><td><strong>1.98</strong></td></tr><tr><td>02</td><td>3</td><td>518</td><td>21</td><td>283</td><td>11.27</td><td>45</td><td>17</td><td>3.75</td></tr><tr><td>03</td><td>3</td><td>455</td><td>20</td><td>258</td><td>11.40</td><td>59</td><td>17</td><td>2.41</td></tr><tr><td>04</td><td>3</td><td>450</td><td>21</td><td>255</td><td>11.98</td><td>36</td><td>18</td><td>2.94</td></tr><tr><td>05</td><td><strong>4</strong></td><td>349</td><td>18</td><td>188</td><td><strong>13.22</strong></td><td>58</td><td>17</td><td>3.52</td></tr><tr><td>06</td><td><strong>2</strong></td><td>296</td><td><strong>15</strong></td><td><strong>128</strong></td><td>11.49</td><td>32</td><td>15</td><td>2.09</td></tr><tr><td>07</td><td>3</td><td>351</td><td>21</td><td>243</td><td>11.65</td><td>48</td><td>15</td><td>2.64</td></tr><tr><td>08</td><td>3</td><td>452</td><td>16</td><td>169</td><td>11.93</td><td>50</td><td>15</td><td>3.08</td></tr><tr><td>09</td><td>3</td><td>456</td><td>21</td><td><strong>284</strong></td><td>11.76</td><td>45</td><td>18</td><td>3.97</td></tr><tr><td>10</td><td>3</td><td><strong>266</strong></td><td>16</td><td>143</td><td>11.85</td><td>30</td><td>15</td><td><strong>7.01</strong></td></tr><tr><td>11</td><td>3</td><td>333</td><td>18</td><td>157</td><td>11.48</td><td>45</td><td>18</td><td>3.36</td></tr><tr><td>12</td><td>3</td><td>338</td><td>21</td><td>216</td><td>12.06</td><td>36</td><td>14</td><td>5.08</td></tr><tr><td>13</td><td>3</td><td>417</td><td>18</td><td>239</td><td>11.10</td><td>55</td><td>17</td><td>2.72</td></tr><tr><td>14</td><td>3</td><td>430</td><td>16</td><td>132</td><td>11.10</td><td>42</td><td>13</td><td>4.03</td></tr><tr><td>15</td><td>3</td><td>484</td><td>20</td><td>264</td><td>12.37</td><td>45</td><td>18</td><td>2.93</td></tr><tr><td>16</td><td>3</td><td>361</td><td><strong>22</strong></td><td>213</td><td>11.66</td><td>35</td><td>14</td><td>3.93</td></tr><tr><td>17</td><td>3</td><td>444</td><td>20</td><td>169</td><td>10.84</td><td>31</td><td>17</td><td>4.26</td></tr><tr><td>18</td><td>3</td><td>368</td><td>18</td><td>190</td><td>12.38</td><td>38</td><td>15</td><td>3.71</td></tr><tr><td>19</td><td>3</td><td>471</td><td>19</td><td>137</td><td>11.29</td><td>35</td><td>16</td><td>2.94</td></tr><tr><td>20</td><td>3</td><td><strong>558</strong></td><td>16</td><td>133</td><td>12.43</td><td>28</td><td>12</td><td>5.56</td></tr><tr><td>21</td><td>3</td><td>352</td><td>16</td><td>146</td><td>11.89</td><td><strong>20</strong></td><td><strong>10</strong></td><td>6.84</td></tr></tbody></table>

**Natural Language Annotation Density.** Each session contains an average of 200.33 natural language descriptions, with the maximum reaching 284. Even the smallest session includes 128 descriptions, indicating consistently high annotation coverage. The average length of each description is 11.67 words, with low variance (std = 0.67), demonstrating a consistent level of granularity. These detailed annotations are not merely labels, but semantically rich phrases that describe activities, sensor interactions, or resident contexts. Furthermore, sessions involve a wide range of activity types: on average, each session includes 41.71 activity instances covering 15.71 distinct types. Some sessions contain up to 63 activity instances and 19 different types, contributing to a diverse annotation space. This level of detail suggests that the dataset offers fine-grained, natural language annotations capable of supporting nuanced reasoning.

**Multi-Resident Complexity and Dynamics.** The dataset captures realistic multi-resident dynamics. Most sessions involve three users (mean = 3.00, std = 0.31), with one session involving four residents and another with two. The number of sensor events per session is substantial (mean = 406.81), ranging from 266 to 558 events, indicating frequent activity and interaction. Additionally, each session utilizes an average of 18.76 distinct sensors (total number is 23), covering a wide range of spatial and functional contexts. The temporal dynamics are also reflected in the variation of activity durations: while the average duration per activity is 3.75 minutes, the standard deviation is 1.35, with durations ranging from 1.98 to 7.01 minutes. This suggests that activities vary significantly in temporal structure and density. These factors together high-

light the dataset's ability to represent complex and dynamic multi-resident scenarios, which are essential for benchmarking reasoning algorithms in real-world smart home environments.

# 5 Experimental Evaluation

In this section, we benchmark MuRAL using state-of-the-art LLM as a baseline for future potential work. We evaluate the results of this benchmark from three perspectives: subject assignment, action description, and activity classification.

## 5.1 Approaches

To evaluate the MuRAL dataset's usability and challenges, we adopted a two-stage LLM-driven approach, largely aligned with the methodology used for LAHAR [5]. Unlike LAHAR, however, we intentionally applied minimal data preprocessing to better highlight the unique characteristics of the MuRAL dataset rather than relying on sophisticated inference techniques.

In the first stage, our goal was to infer the corresponding resident identity and activity description for each sensor event. Specifically, we segmented the event stream into blocks of 10 events and converted each block into a JSON-formatted string. Each JSON item includes the timestamp, sensor information, and a sequence index. We fed these JSON blocks into the first LLM, guided by a carefully designed prompt instructing the model to assign a resident to each event, describe their likely actions, and maintain an up-to-date state for each resident. To ensure coherence across blocks, the final resident states from
one block were passed as context into the next, enabling con-
sistent subject assignment and action descriptions throughout
the session.

In the second stage, we used the resident assignments from
the first stage to separate the sensor events by individual res-
ident. For each resident, we again grouped their events into
blocks of 10 and input them into a second LLM. The input for
each block included the sequence index, timestamp, and action
description. We instructed the LLM to infer high-level activi-
ties based on the described actions. The outputs were aligned
back to the original data using the sequence indices, ensuring
a one-to-one correspondence between generated content and
raw events. This alignment provides a solid foundation for
both qualitative and quantitative evaluation.

The LAHAR method was also used as a reference in our ex-
periments for comparative evaluation. Compared to our ap-
proach, LAHAR performs more extensive preprocessing: it
merges adjacent duplicate sensor events and pairs the activa-
tion and deactivation of the same sensor into a single event
pair. This approach assumes that the merged events were trig-
gered by the same user. When applying LAHAR, we recorded
the indices of these merged events and restored them to the
original event sequence during evaluation to maintain a one-
to-one correspondence with the raw data.

Furthermore, in both stages of its pipeline, LAHAR instructs
the LLM to merge event pairs, effectively summarizing re-
dundant information—such as generating a single sentence to
describe a sequence of events in the bathroom. However, to
ensure a fair and fine-grained evaluation, we disabled this be-
havior in our experiments. Instead, we instructed LAHAR to
explicitly describe and classify each event pair individually.

5.2 Metrics

**Resident Assignment Accuracy** In multi-user activity recognition, each event may involve multiple users, and the model is expected to predict sets of anonymous user identities. Since ground truth (GT) user labels and predicted user labels are not aligned a priori, it is necessary to dynamically infer a one-to-one mapping between GT users and predicted users during evaluation.

Naïve solutions, such as direct label comparison or fixed
global matching (e.g., via Hungarian alignment at the dataset
level), fail in this setting. These methods assume static identity
correspondence and cannot handle partial participation, dy-
namic user appearance, or temporal identity drift. In practice,
user involvement may vary from event to event, and predic-
tions can be inconsistent or partially correct. Without a dy-
namic mechanism, errors from early mismatches can propa-
gate throughout the evaluation, leading to misleading metrics.

To address this, we propose a dynamic individual-level map-
ping algorithm, described in Algorithm 1, that incrementally
builds and updates a GT-to-predicted identity mapping across
events. For each event, we formulate the assignment between
GT and predicted user sets as a maximum matching problem,
solved using the Hungarian algorithm [14] on a scoring matrix.

The score matrix favors consistent mappings while penalizing
identity reassignment and reused predictions. Each GT user is
mapped either to a predicted user or, if no valid match exists,
to a placeholder. Redundant predictions are counted as errors.
Crucially, we allow the mapping to evolve over time while
counting only one error per event, regardless of the number
of mismatches within it.

This strategy provides a principled and robust evaluation
mechanism that reflects both consistency and adaptability, en-
abling accurate assessment of multi-user predictions in com-
plex real-world scenarios.

<caption>**Algorithm 1:** Calculation of Resident Assignment Accuracy</caption>

**Input:** GT sets $G = [G_1, ..., G_n]$, Pred sets $P = [P_1, ..., P_n]$
**Output:** Mapping $M$: GT individual $\rightarrow$ Pred individual, error count $E$
1. Initialize mapping $M$, reverse mapping $R$, error count $E \leftarrow 0$;
2. **foreach** event pair $(G_i, P_i)$ in $G$, $P$ **do**
3. &nbsp;&nbsp;&nbsp;&nbsp;Construct a score matrix $S$ for all pairs $(g, p) \in G_i \times P_i$
4. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;High score for consistent mapping, medium for new, low for conflict;
5. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use Hungarian algorithm to find optimal assignment $A_i \subseteq G_i \times P_i$;
6. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;errorTag $\leftarrow$ False;
7. &nbsp;&nbsp;&nbsp;&nbsp;**foreach** $(g, p) \in A_i$ **do**
8. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**if** $M[g] = p$ **then**
9. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;continue
10. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end**
11. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**else**
12. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Update $M[g] \leftarrow p, R[p] \leftarrow g$; remove any conflicting mappings;
13. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;errorTag $\leftarrow$ True if conflict occurred
14. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end**
15. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end**
16. &nbsp;&nbsp;&nbsp;&nbsp;Map any unmatched $g \in G_i$ to "..." (placeholder);
17. &nbsp;&nbsp;&nbsp;&nbsp;**if** any unmatched $p \in P_i$ **exists** **then**
18. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**errorTag** $\leftarrow$ True
19. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end**
20. &nbsp;&nbsp;&nbsp;&nbsp;**if** errorTag **then**
21. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;$E \leftarrow E + 1$
22. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end**
23. **end**
24. **return** $M, E$

**Cosine Similarity Score** To evaluate the difference between the action descriptions $\hat{A}$ generated by the LLM and the human-annotated descriptions $A$, we use an embedding model to compute the embedding vectors $emb(A)$ and $emb(\hat{A})$ for each pair $(A, \hat{A})$.
We then compute the cosine similarity between the two em-
bedding vectors as a measure of their semantic similarity:

$$
\begin{align*}
\operatorname{sim}(A, \hat{A}) &= \cos (\operatorname{emb}(A), \operatorname{emb}(\hat{A})) \\
&= \frac{\operatorname{emb}(A) \cdot \operatorname{emb}(\hat{A})}{\|\operatorname{emb}(A)\| \cdot \|\operatorname{emb}(\hat{A})\|}.
\end{align*}
$$

Let a session contain *N* action-description pairs (*A*<sub>*i*</sub>, *Â*<sub>*i*</sub>),
where *i* = 1, 2, . . . , *N*. The overall action description score
for the session is computed as the average cosine similarity:

$$
\mathrm{sim}_{\mathrm{session}} = \frac{1}{N} \sum_{i=1}^{N} \mathrm{sim}(A_i, \hat{A}_i).
$$

**Accuracy, F1-score** For the high-level activity classification task, we adopt standard classification metrics, namely Accuracy and F1-score.

5.3 Results

We conducted experiments on the MuRAL dataset using the approach introduced above and evaluated the results based on the previously described metrics. We analyzed the experimental outcomes from three sub-task perspectives: Subject Assignment, Action Description, and Activity Classification.

5.3.1 Subject Assignment

We observe that, for both the LAHAR and baseline ap-
proaches, the 4o model consistently outperforms the 4o-mini
model in terms of subject assignment accuracy. This suggests
that the base model’s capacity plays a crucial role in address-
ing the subject assignment task on the MuRAL dataset. Fur-
thermore, LAHAR significantly improves the assignment ac-
curacy of the 4o-mini model compared to the baseline, indi-
cating that prompt engineering and output format optimization
can effectively enhance the performance of weaker models.

However, the 4o-LAHAR model does not outperform the 4o-
baseline and even shows a slight performance drop. We hy-
pothesize that this may be due to LAHAR’s design choice of
merging repeated or adjacent events from the same sensor into
a single event pair and assuming that such a pair originates
from the same subject. While this assumption holds in simpler
settings, it is less applicable in the MuRAL dataset, where the
complexity of real-world multi-resident scenarios results in a
significant number of adjacent events that do not follow this
pattern.

5.3.2 Action Description

For the fine-grained action-level natural language description
task, LAHAR underperforms compared to the baseline across
both models. This is primarily because LAHAR generates
descriptions based on event pairs—simultaneously describing
both the activation and deactivation of a sensor within a sin-
gle sentence. Such pairing increases the semantic granularity
of each description and deviates from the MuRAL annotation

style, which describes each event individually. As a result, the
descriptions generated by LAHAR tend to have lower simi-
larity to the reference annotations than those produced by the
baseline method, which generates descriptions for each event
separately.

However, LAHAR’s approach also brings benefits: merging
paired events into single descriptions reduces redundant output
and improves generation efficiency. Additionally, we observe
that, for both the baseline and LAHAR methods, the 4o-mini
model consistently produces descriptions with higher similar-
ity to the ground-truth annotations than the full 4o model.

Overall, regardless of the model or method used, generating accurate and detailed action-level descriptions solely from ambient sensor data remains a challenging task, and there is still substantial room for improvement in both precision and expressiveness.

5.3.3 Activity Classification

For the activity classification task, the 4o model consistently
outperforms the 4o-mini model under both the LAHAR and
baseline methods. While LAHAR offers noticeable improve-
ments over the baseline in the 4o-mini setting, it provides little
to no advantage when applied to the larger 4o model. Over-
all, neither the models nor the methods are currently sufficient
to support high-accuracy zero-shot activity recognition using
ambient sensor data.

Our qualitative analysis reveals a key limitation shared by both methods: they treat each user independently when recognizing activities, which leads to a failure to capture inter-user interactions. For example, if User A turns on the television and begins watching it on the sofa, and User B later joins and sits beside A, the system may classify User B’s activity simply as “resting on the sofa” rather than “watching TV”, since the TV was not directly activated by User B. This results in a semantic gap in multi-user collaborative or co-located activities.

Moreover, activities that are not directly linked to specific sen-
sors pose additional challenges. For instance, in the MuRAL
dataset, the activity “using PC” lacks a dedicated PC-related
sensor. When a user engages in this activity at the dining ta-
ble using a personal laptop, the only sensor triggered might
be the chair sensor. This leads to high ambiguity in interpret-
ing the action, as many plausible activities share similar sensor
patterns. Consequently, the language model may fall back on
generating a semantically similar but imprecise label, such as
“resting on the chair”. Such scenarios are common in com-
plex, real-world environments. We present in Figure 4 the full
confusion matrices of the baseline model with both 4o-mini
and 4o models.

These observations suggest that, despite advances in model ca-
pabilities, accurate human activity recognition using ambient
sensors remains highly dependent on sufficient sensor cover-
age and rich contextual information. As models grow stronger,
the demand for informative and context-aware sensor inputs
will become even more critical to support fine-grained and se-
mantically accurate activity inference.
<table><caption>**Table 3:** Comparisons of model performances across 3 tasks.</caption><thead><tr><th>Task</th><th>4o-mini-baseline</th><th>4o-mini-LAHAR</th><th>4o-baseline</th><th>4o-LAHAR</th></tr></thead><tbody><tr><td>Subject Assignment</td><td>0.737</td><td>0.804</td><td><strong>0.826</strong></td><td>0.818</td></tr><tr><td>Description Generation</td><td><strong>0.564</strong></td><td>0.465</td><td>0.563</td><td>0.453</td></tr><tr><td>Activity Classification</td><td>0.186</td><td>0.231</td><td><strong>0.417</strong></td><td>0.413</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Confusion matrices for activity classification using the baseline approach in MuRAL.</figcaption></figure>

# 6 Conclusion

In this paper, we presented MuRAL, a new multi-resident ambient sensor dataset annotated with natural language, designed to unlock the potential of large language models (LLMs) for human activity recognition (HAR) in realistic smart-home settings. Unlike prior datasets, MuRAL provides fine-grained action descriptions, rich contextual annotations, and dynamic multi-user scenarios that closely resemble real-world daily life. Through extensive experiments, we demonstrated the feasibility and limitations of applying LLMs to three essential tasks: subject assignment, action description, and activity classification. Our findings reveal that while LLMs are capable of producing semantically rich and interpretable outputs, challenges remain in multi-user disambiguation and context reasoning, particularly when sensor coverage is sparse or ambiguous.

MuRAL highlights the need for datasets and benchmarks that align with the capabilities and expectations of modern foundation models. For access to MuRAL, please reach out to us via the provided contact information. We hope MuRAL will serve as a valuable resource for advancing the next generation of explainable, language-driven, and socially aware HAR systems. Future work includes integrating multimodal signals,

exploring conversational interfaces for real-time feedback, and co-evolving datasets with LLM architectures.

# Ethics Statement

The MuRAL dataset was collected under strict adherence to European General Data Protection Regulation (GDPR) and French data privacy laws (Loi Informatique et Libertés). All participants provided informed written consent for the collection and use of sensor and video data for research purposes. Video recordings were used solely for annotation purposes and were irreversibly deleted after data annotation. The final dataset is fully anonymized, contains no personally identifiable information, and was approved by the Ethics Committee of Université Grenoble Alpes (CERGA). The dataset is shared with the research community in full compliance with ethical and legal standards.

# Acknowledgement

We would like to express our sincere gratitude to Mélissa COURLA and Sybille CAFFIAU for their invaluable support and assistance in setting up and managing the DOMUS testbed. We are also deeply thankful to all the volunteers who generously participated in the data collection sessions. Their
involvement made this research possible. This work was supported by the LIG Laboratory and Orange Innovation.

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